Comparison of the High Definition Encoder Chips and Architectures
\r\n\tBiomedical application of hydrogels was initially covered by the toxicity of cross linking agents and limitations of hydrogel formation under physiological conditions. Emerging knowledge in polymer chemistry and increased understanding of biological processes resulted in the design of versatile materials and minimally invasive therapies.
\r\n\tNovel, challenging properties of various hydrogels will attract additional attention of researchers in the biological, medical and pharmaceutical field.
\r\n\tIn the last few years there have been developed new methods of preparation of hydrophilic polymers and hydrogels that may be used in the future in biomedical and drug delivery applications. Such efforts include synthesis of self-organized
\r\n\tnanostructures based on triblock copolymers with applications in controlled drug delivery. These hydrogels could be used as carriers for drug delivery if combined with techniques of drug imprinting and subsequent release.
\r\n\tEngineered protein hydrogels have many potential advantages, are excellent biomaterials, are biodegradable and could encapsulate drugs and used in injectable form in order to replace surgery, to repair damage cartilage, in regenerative medicine, or in tissue engineering. Also, they have a potential use in gene therapy.
\r\n\tStill, this field is relatively new.
In multimedia system, there are several video coding standards such as MPEG-1/2/4 [1]-[3], H.264/AVC [4], VC-1 [5], they are the source coding technology basis for digital multimedia applications. Despite of the emerging HEVC standard [6], H.264/AVC is the most mature video coding standard [4] [9]. China Audio and Video Coding Standard (AVS) is a new standard targeted for video and audio coding [7]. Its video part (AVS-P2) had been formally accepted as the Chinese national standard in 2006 [7]. Similar with MPEG-2, MPEG-4 and H.264/AVC, AVS-P2 adopts block-based hybrid video coding framework. AVS achieves equivalent coding performance with H.264/AVC. There are different coding tools and features in different standards. However, the crucial technologies they employed are very similar with coincident framework. These similar standards are MPEG-like video standards.
\n\t\t\t\n\t\t\tIn MPEG-like video encoders, motion estimation (ME) and motion compensation (MC) give a temporal prediction version of the current macroblock (MB). Intra prediction (IP) gives the spatial prediction version. Simultaneously, the predicted MB is coded and followed by transform (DCT), quantization (Q), inverse transform (IDCT), and inverse quantization (IQ). The distorted image is reconstructed with in-loop deblocking (DB) filter. Entropy coding (EC) adopts variable length coding to exploit symbol statistical correlation.
\n\t\t\t\n\t\t\tAVS-P2 is also a MPEG-like standard similar with H.264/AVC [4]. Its major coding flow is similar with those of other MPEG-like standards. There are also some differences between AVS and H.264/AVC. There are five luminance and four chrominance intra prediction modes on the basis of 8x8 blocks in AVS. Also, only 16x16, 16x8, 8x16, and 8x8 MB inter partition modes are used in AVS, in which quarter pixel motion compensation with 4-tap fractional pixel interpolation is adopted. Being different from H.264/AVC baseline profile, the Jizhun profile in AVS supports bidirectional prediction in B frames using a novel “symmetric” mode [7]. Combined with forward, backward, symmetric, and direct temporal prediction modes, there are more than fifty MB inter prediction modes. The industrialization for the AVS standard is being on and leaded by the AVS industry alliance. Efficient AVS video encoder design is important for AVS standard industrialization to dig the standard compression potential.
\n\t\t\tThe modules to be jointly optimized in MPEG-like video encoder framework.
With the technology development and video quality requirement increment, consumer demand is being generated for larger picture sizes and more complex video processing [8]-[10]. High definition (HD) video application has become the prevailing trends. A wide range of consumer applications require the ability to handle video resolutions across the range from 720P (1280x720) and full high definition (full-HD, 1920x1080) to quad full high definition (QFHD, 3840x2160) and even Ultra HD (7680x4320) [15]-[22].
\n\t\t\tHD applications result in higher bit rate and complex video coding [15] [21]. Achieving higher video compression is one important task for video coding expert group and related corporations, especially for HD and super HD applications. Efficient HD MPEG-like video encoder implementation is a huge challenge.
\n\t\t\tH.264/AVC and AVS standards offer the potential for high compression efficiency. However, it is very crucial to design and optimize video encoder to fully dig and explore the compression potential, especially for the HDTV applications. In this chapter, we discuss the design considerations for HD video encoder architecture design, focusing on algorithm and architecture design for crucial modules, including integer and fractional pixel motion estimation, mode decision, and the modules suffering from data dependency, such as intra prediction and motion vector prediction.
\n\t\tAVS and H.264/AVC video encoders may be implemented on platforms such as general CPU or DSP processor, multi-core processor, or hardware platforms such as FPGA (Field Programmable Gate Array) and ASIC (Application Specific Integrated Circuit). For efficient HD video encoder, FPGA and ASIC are well-suited platforms for VLSI implementation. These platforms offer huge hardware computation (macrocells or hardware gate) and on-chip storage (SRAM) resources, which are both important and indispensible for professional HD MPEG-like video encoder implementation.
\n\t\t\t\tThe hardware architectures for MPEG-4 video encoders were reviewed in [11]. Also, there are several only intra-frame encoder architectures reported in [12]-[14]. The predominating VLSI architectures for HD H.264/AVC encoder architectures were reported in the literature. However, algorithm and architecture further optimization is still important and urgent.
\n\t\t\tThere are several challenges as for HD video encoder architecture design, including ultra high complexity and throughput, high external memory bandwidth and on-chip SRAM consumption, hardware data dependency, and complex prediction structures. Moreover, multiple target performance trade-off should be taken into consideration.
\n\t\t\t\tThe first challenge is complexity and throughput. H.264 and AVS requires much higher computation complexity than the previous standards, especially for HDTV applications. There are some coding tools that contribute to performance improvement, however resulting in high computation complexity, such as complex temporal prediction with multiple reference frame (MRF), fractional motion vector (MV) accuracy, and variable block size motion estimation (VBSME), intra prediction with multiple prediction modes, Lagrangian mode decision (MD), and context-based adaptive binary arithmetic coding (CABAC). As a result, the processing throughput is dramatically high. Taking 1080P@30Hz as an example, there are 8160 macroblocks (MB) in one frame, and the MB pipelining throughput is 244800 MBs per second. In QFHD@ 30fps format, the throughout is as four time as that in 1080P@30fps. In the-state-of-the-art architectures [15]-[21], the average MB pipeline interval generally varies from 100 to 500 cycles. Under this constraint, the architecture designs, for IME with large search range and FME with multiple modes, are both huge challenges.
\n\t\t\t\tThe second challenge is the processing sequential flow and data dependency. There are frame, MB, and block level data dependencies. The frame-level dependencies due to I, P, and B frames contribute the considerable system bandwidth. The MB-level sequential flows include intra/inter prediction, MB reconstruction (REC), EC, and DB filter. At the block level, one block intra prediction (IP) is context-dependent with the up, left, and up right blocks. In the reconstruction loop, DCT, Q, IQ, and IDCT are processed in turn. The motion vector prediction (MVP) is context-dependent with the up, left, and up right blocks. These hierarchical data dependencies are harmful for hardware pipelining. It is important to efficiently map the sequential algorithms to parallel and pipelined hardware architectures to improve the hardware utilization and the throughput capacity.
\n\t\t\t\tThird, high SRAM consumption and external memory bandwidth are major challenges. Local SRAM buffers are necessary to achieve data communication among adjacent pipeline stages in pipelined architecture. Reference pixel SRAM buffers for IME and FME are the largest buffer due to the large size search window. Buffer structure and data organization are highly related with hardware architecture. As a result, on-chip buffer structure and data organization are important consideration factors for hardware architecture design.
\n\t\t\t\tExternal memory bandwidth is another challenge. There are huge data exchanges between external memory and on-chip SRAM buffer for real-time video coding. The reference pixel access operations are the largest bandwidth consumers with almost 80% consumption. MRF motion estimation directly doubles the bandwidth consumption and aggravates the bandwidth burden greatly.
\n\t\t\t\tFourth, multiple target performance optimization is another challenge. In terms of hardware architecture efficiency, there are multiple target parameters concerned. Typical target performance parameters are R-D performance, hardware cost, on-chip SRAM consumption, processing throughput, external memory bandwidth, and system power dissipation, etc.
\n\t\t\t\tHow to achieve trade off is critical for architecture design. Multiple target performance parameters are all factors to be considered for architecture design. It is very difficult to satisfy all these constraints and reach optimal trade-off. It is very necessary to make in-depth research at algorithm and architecture level optimization to tradeoff multiple mutually exclusive factors.
\n\t\t\tAs analyzed above, HD video encoder architecture design is a multiple target optimization problem, and challenged by several factors. Among these multiple target parameters, on-chip SRAM size and external memory bandwidth are very crucial. These two targets have important influences on data organization and on-chip buffer structure [23] [24]. Fig.1 gives the inter-relationship among algorithm, architecture, data organization, and buffer structure.
\n\t\t\t\t\n\t\t\t\tThe hardware oriented algorithm is customized under the hardware architecture constraint, with data organization and data flow considered. Hardware architecture is designed with the buffer structure considered according to the algorithm characteristics constraint. Data organization and on-chip buffer structure are jointly designed to achieve efficient data reuse and regular control flow for massive data streaming. On the one hand, efficient data reuse alleviates high memory bandwidth burden and decrease the SRAM consumption. On the other hand, regular control flow simplifies the architecture RTL (Register-Transfer-Level) code implementation.
\n\t\t\t\tAlgorithm, architecture, data organization, and buffer structure
The multiple target performance parameters are complex. The hardware oriented algorithm directly determines the R-D performance. The hardware architecture determines the logic gate consumption. The buffer structure determines the external memory bandwidth consumption, and determines the on-chip SRAM consumption jointly with the data organization. The system power dissipation is more complicate, and determined by the throughput capacity, logic gate, memory bandwidth, and SRAM size. Also, it is directly and indirectly related with algorithm, architecture, data organization, and buffer structure.
\n\t\t\t\tAccording to above analysis, algorithm and architecture should be jointly designed under the multiple target performance trade off consideration. Data organization and on-chip buffer structure are highly related with algorithm and architecture. They should be focused on during the mapping process from algorithm to architecture.
\n\t\t\tIn order to achieve multiple target performance optimization, it is necessary to explore the inter-function mechanism among the multiple targets. Also, how to make exact and fair comparison among multiple target performance parameters is a basic but important problem. It is difficult to build appropriate multiple target performance evaluation models to guide algorithm and architecture joint design. The following factors jointly contribute to this dilemma.
\n\t\t\t\tFirst, different profile and level combination, as well as the video specification are targeted in prevailing architectures [12]-[21]. There are different advanced coding tools in different profiles. As a result, it is not easy to evaluate the multiple target performance of the architectures in different profile and level combinations.
\n\t\t\t\tSecond, there is complex inter-relation among multiple target performance parameters. Logic gate and on-chip SRAM consumption are mutually interdependent, and highly related with the throughput and architecture. System power dissipation is related with logic gate, SRAM, and system clock frequency (throughput). The external memory bandwidth is related with the system throughput and on-chip SRAM. These target performance parameters are all inter-dependent, and it is not easy to accurately measure the inter-influence mechanism for multiple target performance evaluation.
\n\t\t\t\tThird, R-D performance fair comparison is very difficult. On the one hand, R-D performance results reported in the architectures [15]-[21] are derived with different benchmark algorithms. On the other hand, different test sequences are used for R-D performance simulation. Even the same PSNR results reported may correspond to different algorithm performance. PSNR is not the most suitable criterion for accurate video quality assessment.
\n\t\t\t\tFourth, different architectures target for different applications. Some works focus on professional high-end video applications, such as digital TV and broadcasting, in which the compression efficiency is the first target with the highest priority. Some works focus on portable applications, in which power dissipation is the first important target. Different target performance parameters cherish different priority levels in different application targets. This factor is preferred to be considered for multiple target performance evaluation.
\n\t\t\t\tThe above multiple target performance parameters, with different applications, different profile and levels, are the design constraints for multiple target performance optimization.
\n\t\t\tIn AVS and H.264/AVC video encoder, there are several normative modules whose algorithms are deterministic and not allowed for customization. They are transformation (DCT), quantization (Q), inverse quantization (IQ), inverse transformation (IDCT), intra prediction (IP), motion vector prediction (MVP), motion compensation (MC), deblocking (DB) filter, and entropy coding (EC). Among them, DCT, Q, IQ, and IDCT jointly form the reconstruction (REC) loop. There are other four modules whose algorithms are customizable according to the application targets. They are video preprocessing or video preanalysis, motion estimation (ME), mode decision (MD), and rate control (RC). Fig.1 gives the video coding framework with these two types of module partition.
\n\t\t\t\tThe modules with customizable algorithms are very important for architecture design. In VLSI architectures, the REC and IP are usually combined and embedded with the MD module to break the block level data dependency. The MC module is usually combined and embedded with the FME module to reuse the interpolation hardware circuit. As a result, we mainly focus on the four critical modules: IME, FME with MC, MD with IP and REC, and MVP for data dependency in this work. The architectures of the DB and EC modules also influence the throughput, hardware efficiency and power dissipation. Nevertheless, we mainly focus on the modules with customizable algorithm.
\n\t\t\tMotion estimation (ME), including integer-pel ME (IME) and fractional-pel ME (FME), is the most complex module in MPEG-like video encoder. HD ME implementation is highly challenged due to not only large search window, but also new tools such as variable block size ME (VBSME) and multiple reference frames. Moreover, data dependency in block level motion vector prediction (MVP) should be considered for rate distortion optimized ME. Thus, hardware friendly ME algorithm modifications are very important [25] [26]. MVP is combined with the IME and FME modules for algorithm and architecture design.
\n\t\t\t\tFull search algorithm is usually adopted due to its good quality and high regularity [25] [26], and it is well-suited for hardware implementation. However, it is challenged due to large search range. Hardware friendly ME algorithm customization is necessary for co-optimization [25]. Fast algorithms can be classified into several categories [15]-[21]: predictive search, hierarchical search, and reduction in search positions and algorithm simplification techniques.
\n\t\t\t\t\tThe first category is the predictive ME algorithm. If a predictive MV is estimated using MV field correlation, local search can be employed instead of global search. These types of algorithms achieve small SRAM and logic consumption with high throughput. Predictive ME algorithms achieve almost no performance loss in the sequences with smooth motion. However, R-D performance loss is unavoidable in the sequences with complex motion due to MV prediction malfunction.
\n\t\t\t\t\tHierarchical multi-resolution ME algorithm is efficient for HD video coding with large search window [18] [24]. Its idea is to predict an initial MV estimate at the coarse level images and refine the estimate at the fine level images. These algorithms are well-suited for hardware implementation due to control regularity and good performance. Relatively, hierarchical search algorithms achieve better tradeoff between R-D performance and hardware cost.
\n\t\t\t\t\tUnder the assumption that the matching cost monotonically increases as the search position moves away from the one with minimum cost, convergence to the optimal position still can be achieved without matching all candidates. Consequently, computation is reduced by decimation of search positions. The type algorithms are well-suited for software based video encoder with tradeoff between computation and performance. However, they are ill-suited for hardware implementation due to high irregularity. Also, this method usually traps in local minima resulting in performance degradation due to frequent failure of monotonically distribution assumption in sequences with complex motion.
\n\t\t\t\t\tSimplification techniques are proposed and combined with IME algorithms, especially for full search, to alleviate the high complexity in HD cases. Typical methods include simplification of matching criterion and bit-width reduction [15].
\n\t\t\t\tFME contribute to the coding performance improvement remarkably, but the computation consumption is dramatically high. The optimal integer pixel motion vectors (MV) of different size blocks are determined at the IME stage by SAD reuse. At the FME stage, half and quarter pixel MV refinements are performed centered about these integer pixel MVs sequentially.
\n\t\t\t\t\tAlthough the factional candidate motion vectors are no more than 49 points, FME complexity is very high due to complex interpolation calculation and VBSME support. The FME algorithm is customizable. Typical hardware oriented FME algorithms include five categories [15]-[21]: candidate reduction, search order, criterion modification, interpolation simplification, and partition mode reduction.
\n\t\t\t\t\tFirst, shrinking the search range is efficient to reduce the search candidates. There are 49 candidates, comprising of one integer-pixel, eight half-pixel, and 40 quarter-pixel candidates. As shown in Fig.3, 49 candidates may be reduced to 17 candidates, 25 candidates, and 9 candidates, and 6 candidates respectively.
\n\t\t\t\t\tCandidate MVs in different FME algorithms.
Second, search order is important in FME due to data flow design consideration in fraction pixel interpolation. Single-iteration and two-iteration search order are two typical techniques. Full search is usually in single-iteration within 49 or 25 candidates as shown in (a) and (b) in Fig.3, with high data reuse efficiency. Two-iteration is usually employed to search optimal half-pixel MV at the first iteration stage, as shown in (c) and (d) in Fig.3, then quarter-pixels are refined at the second iteration stage. Relatively, data reuse efficiency in two-iteration is lower than that in single-iteration method.
\n\t\t\t\t\tThird, matching criterion modification is employed in some works. SATD + λRMV is the typical criterion, and Hadamard transformation is used to calculate the SATD from inter-prediction residue. RMV is the coding bit cost of the MV residue. λ is the Lagrangian multiplier for rate distortion optimized motion estimation.
\n\t\t\t\t\tFourth, interpolation simplification is employed in some works to alleviate the computation burden. Six-tap and two-tap interpolation filters are used for standard half and quarter pixel interpolations. The interpolation used for fraction pixel MV motion search is allowed for simplification at the cost of R-D performance degradation.
\n\t\t\t\t\tFifth, variabe block size (VBS) partition preselection technology is usually combined with FME algorithm to alleviate the data processing burden accounting for multiple partition modes. In HD video encoders, block partition preselection is acceptable. In some works, only blocks larger than 8x8 are supported to alleviate the throughput burden [15] [26]. Some heuristic measures are employed to exclude some partition modes [16] [20] [21].
\n\t\t\t\tAccounting for the design challenges, they are two types of IME architectures. The first type of architectures are based on zero motion vector (MV) centered search algorithm [15] [17]-[19]. All reference pixels in the search window are buffered in on-chip buffer with large size SRAM consumption. The other types of architectures are based on predefined MV centered local search algorithm [16] [21] [27]. In these works, local search is performed within local search window centered about the predefined center MV (MVc), for example a predicted MV (MVp). As a result, only partial reference pixels are buffered into on-chip SRAM buffer. These architectures achieve small SRAM consumption and fast search speed, however suffering from search accuracy degradation due to inefficient MVc estimation.
\n\t\t\t\t\tThe center MV based local search algorithm is the most suitable solution for HD and ultra HD applications. It is crucial to improve the MVc accuracy to sustain this type algorithm’s advantage. Traditional MV prediction algorithms utilize the motion filed correlation to estimate the center MV. It is efficient for the sequences with regular motion. However, they may malfunction in sequences with complex motion.
\n\t\t\t\t\tAccording to the predominating IME architectures [15]-[22], multi-resolution algorithms are well-suited for HD encoder implementation with good tradeoff between performance and complexity. In this work, we tends to employ multi-resolution search algorithm to search an appropriate candidate center MV to compensate the malfunction due to conventional MV prediction algorithm. Multiple center MV selection is employed to estimate the MVc.
\n\t\t\t\t\tThe proposed predictive based motion estimaiton algorithm is shown in Fig.4. Multi-resolution coarse presearch is employed to presearch a candidate center MV (MVp). Spatial and temporal domain MV median predictions are employed to determine two predictive center candidates (MVs and MVt). The MV of skip mode, MVskip, is also taken as one candidate. As a result, these four candidate center MVs are selected to estimate the center MVc. This measure is adopted to improve the MVc prediction accuracy.
\n\t\t\t\t\tThe proposed multi-resolution algorithm is performed from the coarsest level L2 (16:1 downsampled), and the middle level L1 (4:1 down-sampled), to the finest level L0 (undownsampled) sequentially. The 256 pixels in one MB (at level L0) are shown in Fig.5-(a). They are 4:1 down-sampled to four 8x8 blocks (level L1) indexed by m and n, respectively marked using different symbols:×(mn=00),●(mn=01),▲(mn=10), and ■(mn=11). Similarly, each 8x8 block at level L1 is 4:1 down-sampled to four 4x4 subblocks (level L2) indexed by p and q, respectively marked using red, blue, green and black colors. The three-level down-sampling and the indices (m~q) are shown from (a) to (c) in Fig.5. Similarly, all reference pixels are also down-sampled into sixteen interlaced reference sub-search windows.
\n\t\t\t\t\tThe proposed multiple candidate multi-resolution IME algorithm.
The control flows of the proposed IME algorithm are illustrated in Fig.6. Suppose the whole integer pixel search window is ±SRX × ±SRY. Here ±32 × ±32 is used as an illustration example accounting for page limitation. Motion vector refinement is processed by three successive hierarchical stages as follows.
\n\t\t\t\t\tPixel organization and illustration for multi-resolution IME algorithm.
First, full search is done at level L2 to check all downsampled candidate motion vectors (black points) in the whole search window shown in Fig.6-(a). To accelerate search speed, four-way parallel searches are employed using four downsampled pixel samples (mn=01). As shown in Fig.6-(a), there are four-way parallel matching operations issued. Each way searches four horizontally adjacent candidates by this four-way parallelism. As a result, the proposed algorithm achieves the throughput of sixteen candidates at each cycle at level L2.
\n\t\t\t\t\tSecond, motion vector refinement at level L1 is shown in Fig.6-(b). Only the pixels marked with ●(mn=01) attend in SAD calculation for L1 level refinement. Four refinement centers are shown with four red circles as shown in Fig.6-(a). Four-way local searches at level L1 are simultaneously performed within local search window ±SRXL1 × ±SRYL1 centered about four center candidates respectively. One optimal MV (MVp) is finally selected. Then, the final center MVc is estimated from MVp, MVs, MVt, and MVskip using median estimation.
\n\t\t\t\t\tStructure of the proposed multi-resolution IME algorithm.
Third, variable block size IME is performed at stage 3 at level L0 only within local search window with size of ±SRXL0 ×±SRYL0. The resulting R-D performance degradation is small due to the high MV correlation existing in different size blocks of one MB if the local search window is large enough [21].
\n\t\t\t\t\tIf the system throughput is not enough for real-time coding, for example in QFHD format, N-way hardware parallelism may be employed at L0 level for variable block size IME refinement. N is an integer and determined according to the system throughput. The search window size parameters are as follows: SRX=128, SRY=96, SRXL1=10, SRYL1 =8, and SRXL0=16, SRYL0 =12. These parameters are all customizable according to the application targets and video specification.
\n\t\t\t\t\tCorresponding to the algorithm modification, the MB level pipeline structure should be modified. To improve the throughput efficiency for HD video coding, we deepen the pipeline structure and separate the conventional one-stage IME into three pipeline stages: integer pixel presearch, local search window reference pixel fetch, and local integer pixel motion estimation. The system level pipeline structure will be given in the forthcoming section (system pipeline structure).
\n\t\t\t\t\tThe reference pixels are buffered twice, during the presearch stage and the local integer pixel motion estimation stage. At the presearch stage, only quarter-downsampled reference pixels are buffered into on-chip buffer. At the local integer pixel stage, only the reference pixels in the local small search window centered about MVc are buffered into on-chip buffer.
\n\t\t\t\tFME contributes to the coding performance improvement remarkably, but the computation consumption is also very high. The optimal integer pixel MVs of VBS blocks are determined at the IME stage by SAD reuse. At the FME stage, half and quarter pixel MV refinements are performed centered about these integer pixel MVs sequentially. We adopt two-iteration FME algorithm framework as shown in Fig.3-(c).
\n\t\t\t\t\tOn-chip SRAM consumption for the reference pixels in HD video encoder is dramatically high. To decrease the on-chip SRAM consumption for reference pixels buffering, we propose an efficient buffer share mechanism between IME and FME with algorithm simplification. Only the local search window centered about MVc are buffered in ping-pong structed buffer for IME and FME data reuse.
\n\t\t\t\t\tThere are strong correlations existing in the MVs of different size blocks in the same MB [26]. As a result, there exists a local search window (LSW) which contains almost all displaced blocks needed in the whole window case for FME refinement. Thus, FME only needs to be performed within this LSW.
\n\t\t\t\t\tAnother important problem in hardware oriented FME algorithm is the huge bidirectional interpolation consumption burden. In AVS Jizhun profile, a novel bidirectional prediction, “symmetric” mode, is adopted. In this mode, only forward MV (mvFw_Sym) is coded in syntax stream, while backward MV (mvBw_Sym) is predicted from mvFw_Sym by
\n\t\t\t\t\tHere, BlockDistanceFw and BlockDistanceBw are the temporal distances between the current block and its forward and backward reference frames. mvBw_Sym and mvFw_Sym are all quarter pixel MV. To obtain the fractional pixel displaced block, we need to perform half and quarter pixel interpolation successively. If symmetric mode is adopted in both IME and FME, the interpolation computation cost will be very high, and the normal FME pipeline rhythm is also disturbed. So, simplification for symmetric mode FME is necessary.
\n\t\t\t\t\tAt the IME stage, although the mvFw_Sym is integer pixel accuracy, its corresponding mvBw_Sym is quarter pixel accuracy. Some cycles are desired to finish the quarter pixel interpolation, so this extra cycle consumption will lower the throughput efficiency of the IME module. Thus, symmetric mode is not supported in IME in this work.
\n\t\t\t\t\tSymmetric mode FME refinement is followed after forward and backward individual FME refinements. mvFw_Sym is initialized as the quarter pixel accuracy MV (mvFw_normal) of normal forward FME to calculate the corresponding backward MV in the symmetric mode. There are eight half-pixel and eight time quarter-pixel candidate MVs to be refined in FME. As a result, only eight times half-pixel and quarter-pixel interpolation are needed respectively for forward reference MB, and totally sixteen times half-pixel and quarter-pixel interpolation are needed respectively for the backward displaced blocks. This extra interpolation computation is acceptable and has no conflict with symmetric FME refinement.
\n\t\t\t\tIntra prediction (IP) incurs block level data dependency and makes efficient mode decision (MD) algorithm and architecture design more difficult. In general, the reconstruction loop (REC) is combined with MD. IP is usually arranged with MD at the same pipeline stage. MD algorithm is customized with IP jointly considered.
\n\t\t\t\t\tTo maximize the R-D performance, the most commonly used method is the rate-distortion optimization (RDO) based MD algorithm. It evaluates the cost function (RDcost) of all candidate modes, and the mode with the minimal RDcost is selected for final coding. In some architectures, simplified MD criterion is used instead of RDcost. Three typical simplified criterions are SAD, SATD, and WSAD (weighted SAD). By employing Lagrangian optimization technique, WSAD criterion achieves superior performance than SAD or SATD criterions.
\n\t\t\t\t\tSuppose S and S’ are the original MB and the reconstructed one, and P is the predicted version of a certain mode. Qp and λ are the quantization step and the Lagrange multiplier. Two typical mode decision criterions RDcost and WSAD are described by
\n\t\t\t\t\tSSD is the sum of the squared difference between S and S’, while SAD or SATD is the SAD or SATD value between S and P. RMB is the bits of all syntax elements in the MB. RMBheader is the coding bit of the syntax elements in the MB header.
\n\t\t\t\t\tRDO based MD achieves superior performance due to Lagrangian optimization. In the case of RDcost criterion, genuine distortion is measured with SSD, and genuine rate is used and measured with RMB. Comparatively, only rate is considered in WSAD, in which rate is estimated with SAD and RMBheader. It is the measure simplifications of rate and distortion in WSAD that result in the performance degradation compared with RDcost.
\n\t\t\t\t\tRDO based MD contribute to coding performance considerably. However, the resulting complexity is very high due to abundant candidate modes. SSD between S and the reconstructed block S’ is computed for distortion measure. Rate R is computed by entropy coding (EC). In the end, RDcost is obtained according to R and SSD. The mode with the minimal RDcost is finally selected. The computation costs of R and D for all candidate modes are high. As a result, RDO based MD is computationally intensive. It is challenging to implement architecture design for genuine RDO based MD. Almost all H.264/AVC encoder architectures adopt simplified MD criterion. WSAD, SATD or SAD criterion is used instead of RDcost [15]-[21].
\n\t\t\t\t\tRDO off based MD achieves considerable complexity reduction at the cost of performance degradation. In some works [28], RDO off based mode preselection technique is employed to select partial intra and inter candidate modes, and these candidate modes are further selected using the RDO MD criterion, and the mode with the minimal RDcost is taken as the final coding mode. This combined algorithm achieves better trade off in terms of multiple target performance optimization [28].
\n\t\t\t\t\tRelatively, challenges of RDO based MD in AVS is relatively lower than that of H.264/AVC. It is possible to implement RDO based MD by adopting mode preselection to alleviate the throughput burden. RDO based MD for hardware implementation is challenged by two factors, data dependency and throughput burden.
\n\t\t\t\t\tIn AVS Jizhun profile, five luminance and four chrominance modes are adopted for 8x8 block intra prediction. Thus, there are totally 5×4+4×2=28 blocks to be checked for RDO based intra mode decision in 4:2:0 format videos.
\n\t\t\t\t\tThere are five inter modes: P_skip, 16×16, 16×8, 8×16, and 8×8 in P frames. Comparatively, the inter prediction modes of B frames are more complex. An inter prediction mode of B frame is the combination result of two factors. One is the temporal prediction direction such as forward, backward, and bidirectional (symmetric). The other factor is the MB partition mode such as 16x16, 16x8, 8x16, and 8x8. The temporal prediction direction and MB partition mode combination results in abundant inter coding modes in B frames.
\n\t\t\t\t\tIn this work, mode preselection mechanism is used for throughout burden alleviation.
\n\t\t\t\tWe take two measures to alleviate the serious throughput burden. On the one hand, genuine RDO based MD is adopted for intra mode selection in I frames to sustain the fidelity of anchor frame of the whole GOP, while WSAD based MD is used for intra mode selection in P, B frames based on two considerations. One is that there are many candidate modes to be checked, so candidate mode elimination is highly expected. Another is that the percentages of intra modes is low in P and B frames, so simplified WSAD based intra mode decision in P/B frames results in negligible performance degradation.
\n\t\t\t\t\tOn the other hand, two factors in MB inter prediction modes are separately selected for mode decision. Temporal prediction direction measures the temporal correlation. FME searches the quarter pixel MV justly based on this measure. So, temporal prediction direction is pre-selected at the FME stage using the WSAD criterion. The selected temporal prediction mode may be forward, backward or symmetric (f/b/s). While MB partition mode is to describe the motion consistency of one MB. If four blocks in a MB have consistent motion, the optimal MB partition mode will be 16×16. If four blocks in a MB have highly irregular motion, the optimal MB partition mode will be 8×8. The MB partition mode selection is chosen by the RDO based MD algorithm.
\n\t\t\t\t\tWith the above two simplified measures, candidate modes of P and B frames are largely reduced. The worst case occurs in B frames. The temporal prediction (f/b/s) of each 8x8 mode (B_8x8) in B frames is selected using the WSAD criterion. Then, there are still two modes in each block in B_8x8 partition mode, i.e. direct mode (B_8x8_direct) and f/b/s mode (B_8x8_f/b/s). As a result, there are seven candidate modes to be selected. They are respectively skip/direct, 16x16, 16x8, 8x16, 8x8_f/b/s, 8x8_direct, and the intra mode pre-selected based on the RDO off criterion WSAD.
\n\t\t\t\t\tThe mode matching probability between two and three candidate modes and the optimal mode using the RDO criterion.
There is intrinsic relationship between WSAD distribution of all modes and the optimal mode selected by RDO based MD. We find that the mode with the smallest WSAD value is usually the optimal mode selected with RDcost criterion. Certainly, these two modes mismatch sometimes. If we can preselect partial modes based on the WSAD criterion, what about the matching probability between these preselected modes and the optimal mode in the case of RDcost criterion? We had made investigation on the mode matching statistics using ten standard 720P test sequences. Fig. 7 gives the mode matching probability between two and three candidate modes and the optimal mode, which are selected by WSAD criterion and the RDcost criterion respectively. According to Fig.7, the matching probability varies from 0.6 to 0.8 in the case of two candidate modes; while the probability varies from 0.8 to 0.99 in the case of three candidate modes.
\n\t\t\t\t\t\n\t\t\t\t\tWith this conclusion, we can preselect three inter modes with higher probabilities based on the WSAD criterion to further alleviate the throughput burden. Then, the selected three inter modes, the selected intra mode, and the skip/direct mode are checked using the RDO based mode decision. The simplified algorithm achieves fast decision speed by mode pre-selection and breaking the dependency between prediction direction and MB partition mode.
\n\t\t\t\tResidue coding is adopted for MV coding to utilize the motion field correlation. Thus, a predicted MV is desired for MV coding bit estimation for rate distortion optimized matching cost calculation. Moreover, this MV prediction is simultaneously desired at IME, FME, MD, and EC stages.
\n\t\t\t\tQuarter pixel accuracy MVs of the left, up, up-right, and up-left adjacent blocks in the optimal MB coding modes are employed for MV prediction. In general, IME, FME, and MD are arranged at adjacent pipeline stages. Thus, quarter pixel accuracy MVs of the blocks in the optimal modes are unavailable for MV prediction in IME and FME. This block level data dependency in spatial MV prediction disturbs the normal pipelining rhythm.
\n\t\t\t\tIdeally, quarter pixel MVs are desired for MV prediction at all pipelining stages. This can be easily implemented in software based encoder with sequential processing. However, it is challenged in hardware case with pipeline structure. As shown in Fig.8-(a), MV prediction for RDO based IME for current block (C00) needs quarter pixel MV of its left block (MVA) in the left MB, however it is being on the FME stage, also the MB partition mode is still unknown until FME stage has finished. Similar problem exist in the case of MV prediction for RDO based FME. Thus, algorithm simplification is desired to break this dependency.
\n\t\t\t\tDependency in MV predictor and simplified algorithm, (a): MV predictor for IME, (b): MV predictor for FME, (c): MV predictor for MD.
As shown in Fig.8-(a), incomplete integer pixel MV of the left block in the case of 8x8 MB partition mode is use for C00 MV prediction for IME. Similarly, incomplete quarter pixel MV of the left block is used for C00 MV prediction for FME. Here, the incomplete MV of 8x8 MB partition mode is used because the MB is being at the MD stage. Exact quarter pixel MVs of the neighboring blocks are used for MV prediction of the MD and EC stages.
\n\t\t\t\t\n\t\t\tPipeline structure is crucial for system architecture design. In H.264 and AVS encoder architectures, four-stage MB pipeline structure was adopted in the architectures [15] [16] [19] [20]. The sequential coding modules are arranged into four stages, and they are IME, FME, MD with IP, as well as EC and DB.
\n\t\t\t\tThree-stage MB-pipeline architecture was proposed to decrease the pipeline latency, and save on-chip memory buffer. The architectures in [17] [18] adopted this pipeline structure. FME and IP are combined at the same stage. However, brutal algorithm and high parallelism are desired for algorithm simplification on mode decision to satisfy the system throughput constraint.
\n\t\t\t\tThe proposed system architecture with improved six-stage MB level pipeline structure is shown in Fig.9. In accordance with the predictive MV based local motion estimation algorithm in this work, the IME module in conventional four-stage pipeline structure is separated into three stages: IME presearch, local search window reference pixel fetch, and integer pixel VBSME.
\n\t\t\t\tAs shown in Fig.9, the forward and backward SW reference pixels are stored in Forw. Luma Ref. Pels SRAMs and Back. Luma Ref. Pels SRAMs. Luma Ref, Reg Array and Back. Luma Ref. Reg Array, whose size is very small. Multi-resolution IME predict the center MV (MVp) first, then variable block size ME (VBSME) is performed and the local small Luma SW is transferred simultaneously into the dual-port Local Luma Ref. Pels SRAMs, by which efficient data share between IME and FME is achieved.
\n\t\t\t\tThe chrominance (Chrom) components do not attend in matching cost calculation in IME and FME, thus it is unnecessary to load the whole Chrom SW into on-chip buffer. According to MVp, we can only load the corresponding local small chrom SW, i.e. Local Forw. Chrom Ref. Pels SRAM and Local Back. Chrom Ref. Pels SRAM. Similarly, the Forw. Chrom Reg. Array and Back. Chrom Reg. Array are employed to perform data format transform and buffering. Thus, this local SW buffer saves 80% Chrom SW SRAM consumption compared with the unoptimized case.
\n\t\t\t\tThe proposed pipeline structure and system architecture in MPEG-like video encoder
The quarter pixel interpolation versions in the displaced blocks of all possible inter mode are buffered in the Luma Pred. Pels SRAMs (part I and II) and Chom Pred. Pels SRAM (part I and II) to implement data share between FME and IP/MD stages.
\n\t\t\t\tTo achieve circuit reuse of the residue coding and the EC loops between IP/MD and EC/DB stages, the MB CodeNum SRAM is employed to store the CodeNum fields of all coefficients in the blocks of the selected optimal mode. Thus, bitstream can be easily generated at the following EC stage according to the CodeNum using Golomb exp-coding, and the coded bitstream is buffered in the Bitstream SRAM to wait for external SDRAM bus transactions.
\n\t\t\tIn HD and ultra HD video encoders, multiple parallel processing element (PE) arrays are usually desired to improve throughput. Three-level hierarchical sequential MV refinement is employed to improve the search accuracy. So, it is preferred to adopt reconfigurable PE array structure to achieve efficient PE reuse at adjacent levels.
\n\t\t\t\tInteger pixel presearch performs successive refinements from level L2 to level L1. The block diagram of the proposed IME presearch architecture is given in Fig.10. It should be mentioned that integer pixel presearch only target for the center MV (MVc) estimation, and variable block size motion estimation is not adopted here.
\n\t\t\t\t\tThe basic unit for motion estimation is processing element (PE), which performs SAD (sum of absolute difference) calculation for one pixel. Sixteen parallel processing element units are combined as a group i.e. processing element array (PEA). The task of processing element array (PEApq) is to calculate SAD for 4x4 block indexed by p and q shown in Fig.5-(c), which is 16:1 down-sampled from level L0. Four-way parallel processing element array, consisting of 64 parallel processing elements, work as a group as a processing element array subset (PEAS), accounting for SAD calculation of one 8X8 block (mn=01). Search luminance reference pixels and current MB are fetched from external memory and inputted to the luminance reference pixel buffer (LRPB) and current Sub-MB register (CSMR) respectively.
\n\t\t\t\t\tIME presearch controller accepts encoding parameters from MB controller and main processor, and coordinates all sub-modules for multi-resolution MV refinements. SAD values and MV costs are inputted to the WSAD adder tree and four-input WSAD comparator for SAD reuse and MV selection. Four-way PEAS parallelism structure is employed to achieve 4X speed to cover large search window for real-time HD video coding.
\n\t\t\t\t\tThe architecture for integer pixel presearch.
At the level L2 stage, four-way parallelism is employed with 4x4 processing element arrays, totally 256 processing element (PE) units. This two-dimension PE arrays achieve the throughput of sixteen candidates each cycle at level L2 as shown in Fig.6-(a).
\n\t\t\t\t\tAt the level L1 stage, four PE array modules (PEA00~PEA11) are combined to implement one PE array subset (PEAS). As a result, sixteen parallel PEA units are mapped to four-way PEAS units to achieve 4-way parallel local refinement at level L1, as a result achieving the throughput of four candidate MVs each cycle at level L1.
\n\t\t\t\tLocal integer pixel variable block size IME is at the third pipeline stage. Only block size no smaller than 8x8 is considered due to the observation that smaller block size partition achieves trivial performance improvement in HD cases with high complexity [17] [22].
\n\t\t\t\t\t\n\t\t\t\t\t\tFig. 11 gives the whole structure for integer pixel variable block size IME. Triple-buffered SRAM is employed to store the luminance reference pixels centered around MVc. This structure supports simultaneous access for three clients: next MB reference pixel refreshment, current MB variable block size IME, previous MB FME.
\n\t\t\t\t\tHere, the 256-PE array is the basic unit for block matching cost calculation, and it calculates the SAD value for one candidate motion vector, 256 original and reference pixels attend in SAD calculation. In general, the larger the local search window size, the higher rate distortion performance achieved. In this work, local search window with size of 32 x 24 is used for local refinement. If only one-way 256-PE array is employed, the throughput is only one candidate MV each cycle. That means that at least 768 cycles are desired for one MB processing. In order to achieve high throughput capability, N-way parallelism may be employed. In this work, N=2 and N=3 are adopted for 1080P and QFHD format respectively.
\n\t\t\t\t\tThe structure for integer pixel variable block size motion estimation.
In order to support variable block size IME, 8x8 block is taken as the basic processing unit. Variable block size IME is implemented by employing SAD reuse [25]. Thus, 256-PE array is combined with SAD adder tree for SAD reuse to implement variable block size IME, i.e. 256-PE Array SAD Adder Tree, as shown in Fig. 11. There are nine possible MB partition modes, and N adjacent motion vectors are simultaneously searched. Here, nine partition blocks are respectively 16x16, 16x8_1, 16x8_2, 8x16_1, 8x16_2, 8x8_1, 8x8_2, 8x8_3, and 8x8_4. As a result, nine SAD values of N adjacent motion vectors are simultaneously inputted into the module, titled as Nine-parallel N-input WSAD Adder and Comparator, to select nine optimal motion vectors for nine partition blocks. This architecture is similar with work in reference [15]. Variable block size mode partition is determined at the FME stage.
\n\t\t\t\tAn important problem in mode decision VLSI architecture design is the block level data dependency due to intra prediction. This data dependency breaks the normal pipeline rhythm for intra prediction and mode decision. An intelligent mode decision scheduling mechanism is proposed in Fig.12 and Fig.13 to eliminate the data dependency in P/B and I frames respectively.
\n\t\t\t\t\tInter mode decision scheduling in P and B frames is used as the illustration example shown in Fig.12. First, intra modes of B00, U and V blocks are successively fed to the pipeline for RDcost estimation. Then four luminance blocks and U, V blocks of the skip/direct modes are followed. Suppose T is the block level pipeline period in the MD architecture. At the time of 6T, the intra mode of B00 has finished the pipelining and the reconstructed pixels are ready. During the period from 7T to 8T, the intra modes of B01 is preselected based on WSAD criterion and then intra mode RDcost calculation for B01 can initiate at the time of 10T. Using the same mechanism, intra mode RDcost calculation of B10 and B11 are inserted between luminance blocks and initialized at the time of 17T and 24T. With this intelligent pipeline scheduling strategy, the data dependency problem of intra prediction is solved in P and B frames with 100% hardware utilization efficiency.
\n\t\t\t\t\tSimilarly, the intra mode decision scheduling mechanism in Fig.13 is implemented with inevitable utilization discount, in which the period from 15T to 18T is idle to wait for pixel reconstruction. The RDO based intra mode decision for I frames can achieve 85.7% hardware utilization efficiency.
\n\t\t\t\t\tThe pipeline scheduling strategy for intra and inter mode decision in P and B frames.
The pipeline scheduling strategy for intra mode decision in I frames.
The proposed VLSI architecture for RDO based mode decision is given in Fig.14. Seven prediction versions of the current MB are buffered in the Ping-pang buffers for data share between FME, IP, and MD. Seven prediction MB buffers (Pred. Pels. Buf.) from no. 1 to no. 7 store the predictions of the 16x16_f/s/b, 16x8_f/s/b, 8x16_f/s/b, 8x8_f/s/b, 8x8_direct, intra_preselected and direct/skip modes in P and B frames, In each mode, there are six blocks B00~B11, U, and V. Also, the current MB is also buffered for fluent MD pipelining.
\nThe proposed VLSI architecture of RDO based mode decision.
To achieve the desired throughout at MB level mode decision pipeline, eight pixels of one line in the original block and its predicted block are fetched from the buffers in each cycle and fed into the residue generation (residue Gen.) module to calculate the residue in parallel. The integer DCT in AVS is based on 8x8 block. Horizontal DCT (Hor. DCT) and vertical DCT (Ver. DCT) should be processed in turn to implement the butterfly based fast DCT. Thus, Hor. DCT and Ver. DCT are arranged into adjacent block level pipeline stages to achieve high throughput with the transpose DCT buffer. Eight residue pixels in one line are fed into the Hor. DCT module in parallel.
\n\t\t\t\t\tQuantization (Quant) in AVS needs two multipliers with wide bit width. If eight-way parallel structure is employed. The circuit consumption of multiplexers will be high. Thus, Four-way parallel structure is adopted for the Quant module. Different data throughput rate between Ver. DCT and Quant are buffered and balanced by the Quant buffer. The quantized coefficients are buffered to the inverse quantization (Inver. Quant) module and zigzag buffers simultaneously with necessary data store and format transform for the following zigzag scan and Inver Quant modules. Inverse quantization is very simple, thus it is combined with Hor. IDCT at the same stage. Hor. IDCT and Ver. IDCT are similar with those of DCT. The Ver. IDCT module produces the reconstructed residue, which is fed to SSD calculation (Calcu.) module for SSD calculation and MB reconstruction by the reconstructed residue buffer.
\n\t\t\t\t\tThe other parallel data processing loop is the mode preselection. The intra mode in P and B frames and the inter modes are preselected based on WSAD criterion. During the MD stage, mode pre-selection is simultaneously done by employing the eight-pixel parallel WSAD calculation, block and MB level WSAD adder, and the WSAD based mode preselection modules. WSAD offset table interface is used for algorithm optimization for WSAD based mode preselection in the future.
\n\t\t\t\t\tThe codenum fields of one MB of the optimal mode are buffered in the codenum buffer for data share between MD and the following entropy coding (EC), which generates the final bitstream simply according to codenum fields instead of quantized coefficients using Exp-Golomb coding. Redundant run-level, table selection, VLC table are eliminated by codenum data share between MD and EC instead of quantized coefficients.
\n\t\t\t\tThe whole modified video coding algorithms are developed and evaluated with hardware implementation consideration for Jizhun profile AVS video encoder. The modifications includes the proposed hierarchical ME algorithm, the simplified MV prediction and symmetric FME algorithms, the simplified WSAD based intra mode decision in P and B frames, the simplified inter mode decision algorithm. The modified whole video encoder is compared with the anchor algorithm, which is the standard RM52J with full search and fully RDO based MD (RM52J+FS). RM52J was developed only for syntax compliance test and verification. The source code efficiency is low. Its C model version was developed with efficient data structure and coding style. The simulation results are given in Fig.15.
\n\t\t\t\tThe rate distortion curves of typical 720P test sequences.
Compared with the optimal anchor without any simplification, the average PSNR degradation of the proposed algorithms is generally smaller than 0.1dB. The worst case occurs in the case of the “Spincalendar” sequence with complex motion and prediction modes. The worst PSNR degradation is smaller than 0.15dB. The major PSNR degradation is derived from the IME and MD algorithm simplifications. It should be emphasized that the reference benchmark is optimal algorithm with full search motion estimation and fully RD optimized mode decision. This quality loss is achieved with considerably hardware implementation convenience. The subject visual quality in HD case is almost negligible.
\n\t\t\tThe proposed architecture was implemented with Verilog-HDL language and synthesized by Design complier with SMIC 130nm 1P9M standard cell library. Table 1 shows the hardware cost of the proposed architecture and comparisons to other typical reference designs. The profile and level, video format, reference frame number, variable block partition mode, pipeline structure, throughput, system frequency, gate count, and SRAM consumption are also taken as comparison factors.
\n\t\t\t\t\n\t\t\t\tAccording to the results in table 1, the system throughput efficiency is only lower than that of the architecture in [21]. The logic cost and SRAM consumption of this work is competitive to other reference works.
\n\t\t\t\t\n\t\t\t\tAVS Jizhun profile is equivalent to the main and high profile of H.264/AVC. B frame is supported in the proposed architecture. Two reference frame motion estimation is supported in P frame coding. As a result, the computation and throughput burden is 2X as the works with only one reference motion estimation.
\n\t\t\t\t\n\t\t\t\t\t\t\t | T.C. Chen [15] | \n\t\t\t\t\t\t\tH.C.Chang [16] | \n\t\t\t\t\t\t\tZ. Y. Liu [17] | \n\t\t\t\t\t\t\tY. K. Lin [18] | \n\t\t\t\t\t\t\tYi. H. Chen [19] | \n\t\t\t\t\t\t\tL.F. Ding [20] | \n\t\t\t\t\t\t\tProposed | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\tProfile/level | \n\t\t\t\t\t\t\tBaseline@ Level3.1 | \n\t\t\t\t\t\t\tBaseline@ Level 3.1 | \n\t\t\t\t\t\t\tBaseline@Level4 | \n\t\t\t\t\t\t\tHigh@ Level4 | \n\t\t\t\t\t\t\tHigh/ SVC | \n\t\t\t\t\t\t\tMultiview/High | \n\t\t\t\t\t\t\tJizhun | \n\t\t\t\t\t\t
Video format | \n\t\t\t\t\t\t\t720p@ 30fps | \n\t\t\t\t\t\t\t720p@ 30fps | \n\t\t\t\t\t\t\tFull HD | \n\t\t\t\t\t\t\tFull HD | \n\t\t\t\t\t\t\tFull HD | \n\t\t\t\t\t\t\tFullHD /QFHD | \n\t\t\t\t\t\t\tFull HD @60i | \n\t\t\t\t\t\t
Ref. frame | \n\t\t\t\t\t\t\t1/4 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t
VBS Mode | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tabove 8x8 | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tabove 8x8 | \n\t\t\t\t\t\t
Intra Mode | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t\tAll | \n\t\t\t\t\t\t
Pipeline | \n\t\t\t\t\t\t\t4 stage | \n\t\t\t\t\t\t\t4 stage | \n\t\t\t\t\t\t\t3 stage | \n\t\t\t\t\t\t\t3 stage | \n\t\t\t\t\t\t\t4 stage | \n\t\t\t\t\t\t\t8 stage | \n\t\t\t\t\t\t\t6 stages | \n\t\t\t\t\t\t
Throughput(cycles/MB) | \n\t\t\t\t\t\t\t1000 | \n\t\t\t\t\t\t\t1000 | \n\t\t\t\t\t\t\t820 | \n\t\t\t\t\t\t\t592 | \n\t\t\t\t\t\t\t678 | \n\t\t\t\t\t\t\t312 | \n\t\t\t\t\t\t\t600 | \n\t\t\t\t\t\t
frequency | \n\t\t\t\t\t\t\t108Mhz | \n\t\t\t\t\t\t\t108MHz | \n\t\t\t\t\t\t\t200Mhz | \n\t\t\t\t\t\t\t145Mhz | \n\t\t\t\t\t\t\t166Mhz | \n\t\t\t\t\t\t\t280Mhz | \n\t\t\t\t\t\t\t150Mhz | \n\t\t\t\t\t\t
Logic Gate | \n\t\t\t\t\t\t\t923K | \n\t\t\t\t\t\t\t470K | \n\t\t\t\t\t\t\t1140K | \n\t\t\t\t\t\t\t593K | \n\t\t\t\t\t\t\t2079K | \n\t\t\t\t\t\t\t1732K | \n\t\t\t\t\t\t\t1230 | \n\t\t\t\t\t\t
SRAM Size | \n\t\t\t\t\t\t\t35KB | \n\t\t\t\t\t\t\t13.3KB | \n\t\t\t\t\t\t\t108.3KB | \n\t\t\t\t\t\t\t22KB | \n\t\t\t\t\t\t\t81.7KB | \n\t\t\t\t\t\t\t20.1KB | \n\t\t\t\t\t\t\t29KB | \n\t\t\t\t\t\t
Power | \n\t\t\t\t\t\t\t785mW | \n\t\t\t\t\t\t\t183mW | \n\t\t\t\t\t\t\t1409mW | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | 411mW | \n\t\t\t\t\t\t\t522mW | \n\t\t\t\t\t\t\t764mW | \n\t\t\t\t\t\t
CMOS Technology | \n\t\t\t\t\t\t\tUMC .18um 1P8M | \n\t\t\t\t\t\t\tTSMC 0.13um | \n\t\t\t\t\t\t\t0.18um 1P6M | \n\t\t\t\t\t\t\tUMC 0.13um | \n\t\t\t\t\t\t\tUMC 90nm | \n\t\t\t\t\t\t\tTSMC 90nm | \n\t\t\t\t\t\t\tUMC .13um 1P8M | \n\t\t\t\t\t\t
Comparison of the High Definition Encoder Chips and Architectures
Fair R-D performance is not easy. Different reference codes and different test sequences are used for rate distortion performance comparison. Comparison is expected using the same test sequences with the same benchmark. More important, the frame level average PSNR results are used in current works. PSNR may not be accurate for perceptual video quality. As a result, the PSNR comparison to reference works are not provided in this work due to the fact that fair comparison.
\n\t\t\t\tMulti-resolution motion estimation and partial RDO based mode decision are employed in this work. As a result, the coding performanc of this work is competitive to other works with brunt algorithm simplifications on motion estimation and mode decision.
\n\t\t\tIn this chapter, we propose systematic works for efficient high definition MPEG-like, such as AVS, video encoder VLSI implementation. VLSI Implementation challenge, algorithm and architecture joint design, and multiple target performance optimizations are reviewed and analyzed. Hardware oriented algorithm customizations are made for the crucial algorithm customizable modules, including integer pixel and fractional pixel motion estimation, mode decision, intra prediction, and motion vector prediction. Then, hardware architectures of the crucial modules, motion estimation with motion vector prediction, mode decision with intra prediction and reconstruction loop, are proposed. Algorithm performance and multiple target performance are respectively reported. This work targets for high definition AVS video encoder architecture. However, the algorithms and architectures are well-suited for H.264/AVC, even HEVC video encoder design.
\n\t\t\tPerspectives and future trends in video encoder architecture design may focus on the following factors. First, low-power video encoder architecture is desired for in mobile systems or portable applications. Low power algorithm and architecture design are one emerging issue. Second, professional video encoder architecture is preferred in the high-end applications such as the broadcasting enterprises. Third, algorithm and architecture joint design will be an important trend for HD video encoder architecture. Fourth, algorithm and architecture design for ultra HD and HEVC video encoder will become one research trend.
This work was supported by NSFC 60802025, ZJNSF Y1110114 LY12F01011, S&T project of Zhejiang province 2010C310075, and the open project of State Key Laboratory of ASIC&System of Fudan University 10KF010, and the open project of SKL of Novel Soft Technology, Nanjing University (KFKT2012B09).
Retina, which forms the innermost layer of the eyeball, is considered as the end organ of the central nervous system. Macula, located in the central and posterior part of retina, possesses the highest concentration of photoreceptors and, therefore, is responsible for central vision and high-resolution visual acuity (VA). The fovea is a tiny pit in the center of the macula and in charge of the central, sharpest vision. Unfortunately, the macula is more prone to experience degenerative changes with age, such as age-related macular degeneration (AMD), leading to visual impairment.
\nDue to the increase of life expectancy worldwide, the size of aging population will become larger in the coming decades [1]. According to “World Population Prospects: The 2017 Revision” released by the United Nations, the number of persons aged 60 or above will more than double, from 962 million in 2017 to 2.1 billion by 2050. The number of people aged 80 or above is predicted to triple by 2050, from 137 million in 2017 to 425 million by 2050 [2]. In mainland of China, people aged above 65 represented 11.4% of the total population in 2017 [3]. As a consequence, the increasing prevalence of AMD will be foreseen globally in the future.
\nIn 2015, it was estimated that AMD was the fourth leading cause of blindness and the third most common cause of moderate to severe visual impairment globally [4]. The meta-analysis performed by Wong and collaborators has estimated that the number of persons with AMD will increase from 196 million in 2020 to 288 million in 2040 worldwide [5]. A similar trend is observed in the projected number of individuals affected by AMD in China, rising from 31.23 million in 2020 to 55.19 million in 2050 [6]. In addition, the prevalence of late AMD did not show significant difference among Asian, European, and North American studies, whereas the number of individuals with early AMD was more in European and North American studies than in Asian studies [7].
\nThe eye is composed of three layers, which are the inner retina layer, middle vascular choroid layer, and outer fibrous sclera layer, respectively. Retina, the innermost layer of the eye, consists of two parts: the inner transparent neurosensory retina and outer pigmented epithelial layer—the retinal pigment epithelium (RPE). There is a potential space between neural retina and RPE, called subretinal space. In the neural retina, the neural cell bodies are situated in three layers (Figure 1), including the outer nuclear layer (ONL) occupied with nuclei of photoreceptors; the inner nuclear layer (INL) filled with nuclei of horizontal, bipolar, and most of the amacrine cells; as well as ganglion cell layer (GCL) containing nuclei of retinal ganglion cells and the rest of displaced amacrine cells. Additionally, axons and dendrites of these retinal neurons constitute two synaptic layers: the inner plexiform layer (IPL) and outer plexiform layer (OPL) [8]. The RPE cells form a continuous polarized cell monolayer with its apical surface adjacent to the outer segment apices of the photoreceptors and its basal aspect lying on supportive substrate Bruch’s membrane.
\nRetinal layers of the human eye.
Retinal neurons are mainly distributed in three layers: ONL with nuclei of photoreceptors; INL with nuclei of horizontal, bipolar, and most of the amacrine cells; and GCL with nuclei of retinal ganglion cells and the rest of displaced amacrine cells. Additionally, axons and dendrites of these retinal neurons constitute two synaptic layers, including IPL and OPL [8].
\nPhotoreceptors are divided into two types: rods, which are dominated in the peripheral retina and responsible for dim light vision and detecting movement and contrast, and cones, which are dominated in the macula, especially the fovea (only cones), and are responsible for bright light vision and sensing color vision and resolution. Outer segments of the photoreceptor are generated by the cell body, and the distal parts of outer segments are phagocytosed by RPE every day. There are abundant mitochondria in inner segments to supply energy for these cells with high metabolic rate. Bruch’s membrane, where the RPE cells are tightly attached, consists of five layers including, from inner to outer, the RPE basal lamina, inner collagenous zone, elastic layer, outer collagenous zone, and basement membrane of the choriocapillaris [9].
\nIn brief, the function of the retina is to convert the external light signals into electrical impulses by photoreceptors. The impulses, which are partly integrated locally by horizontal cells and amacrine cells, are then processed by bipolar cells and sent to ganglion cells which further transmit them to the brain. Moreover, RPE is crucial for maintaining the microenvironment of neural retina by exchanging nutrients and wastes between neural retina and choroid, secreting numerous growth factors, phagocytosing shed photoreceptor outer segments (POS), absorbing light and converting all-trans-retinal into 11-cis-retinal [10]. Any defects in any of these functions may result in retinal degeneration, deficits of visual function, and eventually loss of sight.
\nA variety of classification systems are employed for both clinical and basic research of AMD. However, the most commonly used classification is the one defined by the Age-Related Eye Disease Study (AREDS) in terms of the characteristics of drusen (yellow deposits in the macula), abnormal hypo- or hyperpigmentation in the first eye, and how it affected the fellow eye. Based on the above criteria, AMD is classified into 4 categories: (1) no clinical manifestation of AMD if there was no drusen or only non-extensive, small drusen (<63 μm in diameter) in both eyes; (2) mild AMD classified by extensive small drusen, non-extensive intermediate drusen (63–124 μm in diameter), or abnormalities of pigments in at least one eye; (3) intermediate AMD characterized with large drusen (>124 μm in diameter), extensive intermediate drusen, or noncentral geographic atrophy (GA) in at least one eye; (4) advanced AMD defined as central GA or choroidal neovascularization (CNV) resulting in VA less than 20/32 [11, 12, 13].
\nThere are two clinical types of advanced AMD: a non-exudative or atrophic (dry) form, accounting for 90% of AMD, and an exudative (wet) form, accounting for only 10% of AMD. The atrophic form is characterized by progressive degeneration of RPE cells and photoreceptors in the macula, affecting central vision to varying degrees over months or years [14, 15]. The exudative form is associated with CNV in the submacular area and subsequent retinal hemorrhage due to leakage of these newly formed fragile blood vessels, leading to severe central vision loss within a very short period of time [15].
\nAt the early stage of AMD, patients are normally asymptomatic and may be diagnosed by the presence of round, yellowish drusen through the routine ophthalmological examinations. Patients with CNV usually suffer from sudden loss of vision, describing unexpected deterioration of central visual field, distorted straight line (metamorphopsia), and/or a dark area in the central visual field (scotoma). On fundus examination, macular edema and hemorrhage are observed and fluorescein angiography shows leakage. In non-exudative AMD, it takes years for patients to develop visual loss gradually, and fundus examination shows a well-defined RPE atrophic area with depigmentation (Figure 2) [15].
\nRepresentative fundus of AMD patients. (A) Non-exudative form of AMD with a well-defined atrophic region and (B) exudative form of AMD with retinal hemorrhage and macula edema [16].
A number of risk factors are thought to be related to the pathogenesis of AMD, such as increasing age, smoking, family history of AMD, overweight or obesity, cataract and cataract surgery, eye exposure to the sunlight, etc. Of these factors, age is the strongest risk factor, with around 30% of cases older than 85 years diagnosed with AMD in US [17, 18]. The risk of developing AMD was reported to be more than triple in patients above 75 years than patients aged between 65 and 74 years [18, 19]. In populations of European ancestry, the projected prevalence of late AMD was 0.08% at age 50, 0.33% at age 60, 1.38% at age 70, 5.60% at age 80, and 20.10% at age 90, respectively [20]. Similarly, the estimated prevalence of any AMD increased from 2.44% in persons at the age of 45–49 years to 18.98% in persons at the age of 85–89 years [6].
\nEthnicity and family history are also strongly related with AMD prevalence. The highest rate was reported in Caucasians, followed by Hispanics and Asians, while African Americans showed the lowest morbidity rate [21]. Increased risk of AMD was observed in individuals with positive family history, approximately three to six times higher than it was for those from the general population [22]. Furthermore, genetic studies have revealed 34 loci linked to AMD until now, of which complement factor H (CFH) on 1q31.3, age-related maculopathy susceptibility 2 (ARMS2) on 10q26, and CFB/C2 on 6q21.3 were the most discussed genes [15, 23, 24].
\nThe strong relationship between cigarette smoking and development of AMD has been widely accepted [17, 25, 26]. Several studies revealed that current smoking was strongly associated with the progression of AMD [27]. Moreover, past smoking was reported to be a risk factor to develop AMD as well, especially in the rural areas [28]. There was an increased risk for individuals with high intake of several types of fat, such as omega-6 polyunsaturated fatty acids and saturated fats, whereas diets rich in monounsaturated fatty acids might reduce the prevalence of AMD [29]. Inadequate uptake of antioxidants also resulted in AMD, and numerous studies have suggested that antioxidant supplementation, including vitamins, lutein, zeaxanthin, beta-carotene, zinc, etc., helped to halt the progression of AMD to some extent [12, 30, 31].
\nThe effects of sunlight exposure on the occurrence of AMD are controversial. A study performed by Khan and colleagues demonstrated that no significant association between light exposure and AMD was observed when comparing patients at the end stage of AMD with spouse controls [32]. However, other studies suggested that damages induced by either ultraviolet (UV) or visible sunlight might lead to AMD [33, 34, 35]. There was conflicting evidence regarding the role of cataract surgery in the progression of AMD [36]. Earlier, a large population-based research indicated a stronger relationship between cataract surgery with the development of end-stage AMD, especially neovascular AMD, in older patients [36, 37]. On the contrary, several recent clinical studies failed to demonstrate similar results [36, 38].
\nIn addition to the risk factors mentioned above, other relevant factors included hypertension, cardiovascular diseases, iris color, hyperlipidemia, diabetes, alcohol consumption, and so on [22]. According to a systematic review of 18 prospective and cross-sectional studies and 6 case-control studies engaging 113,780 individuals, advancing age, cigarette smoking, previous cataract surgery, and family history of AMD were considered as the strong risk factors of AMD, while hypertension, cardiovascular disease, overweight or obesity, and elevated plasma fibrinogen were regarded as the moderate risk factors [17].
\nAMD is regarded as a complicated multifactorial disease, although the exact pathogenesis still remains poorly understood. It is generally recognized that impairment of aged RPE cell functions served an important role in the progression of non-exudative AMD. The aged RPE cells were less efficient in phagocytosing and degrading POS, resulting in the progressive accumulation of lipofuscin consisting of phagosomal and lysosomal constituents in the cytoplasm. The cytotoxic elements in lipofuscin, such as bisretinoid fluorophore, were able to generate reactive oxygen species leading to the damage of DNA, lipids, and proteins [39].
\nAnother age-dependent change was the formation of focal extracellular yellow deposits known as drusen, the hallmark of AMD, between the RPE and Bruch’s membrane. Accumulation of drusen relied on the changes in the permeability of Bruch’s membrane due to the decline of RPE cell functions with aging [40]. In addition, it has been estimated that breakdown of choriocapillaris, which was next to RPE and Bruch’s membrane, resulted in insufficient elimination of extracellular wastes causing drusen [41]. According to their shape and size, drusen are separated into small (<125 μm), round hard drusen with well-defined borders, and relatively large, (125–250 μm) soft drusen with poorly defined borders. Soft drusen, especially with depigmentation or pigment abnormalities, was considered to be an indication to give rise to severe vision loss at the late stage.
\nThe mechanism of drusen causing adjacent RPE and photoreceptors’ damage was not only dependent on the structural disturbance of RPE and photoreceptor by them, but also on the indirect effects through stimulation of local inflammation and immune system [42, 43]. Components in complement pathway and inflammatory processes were observed in drusen and aged RPE cells that are closely related to drusen [42]. It was demonstrated that in AMD, genetic variation of factor H gene (HF1/CFH) induced the abnormal activities of factor H (the inhibitor of complement cascade) in drusen, leading to the activation of complement pathway and subsequent inflammation in subretinal tissues [44]. Furthermore, in AMD patients, decreased CFH level was reported in smokers than nonsmokers, suggesting the activation of complement cascade might contribute to the significantly higher risk of AMD progression in cases with smoking [45].
\nDICER1, the ribonucleic acid (RNA)-cleaving enzyme, was decreased in RPE cells in cases with non-exudative AMD. A group of researchers in the University of Kentucky suggested that the lower level of DICER1 led to the lower rate of Alu RNA degradation in RPE cells. Therefore, accumulation of Alu RNA in cytoplasm resulted in RPE cytotoxicity and consequent degeneration via activating NLRP3 inflammasome [46, 47].
\nIn exudative AMD, local nonspecific inflammation stimulated the upregulation of angiogenic factors, such as vascular endothelial growth factor (VEGF), and/or downregulation of anti-angiogenic factors, such as pigment epithelium-derived factor (PEDF), causing CNV development in the avascular outer 1/3 retina. The new vessels are fragile, thus macular edema and hemorrhage occur due to the leakage of these new blood vessels, which finally resulted in the fibrovascular scars in the macula [41, 48].
\nDue to the dysfunction of aged RPE cells, especially the failure of POS phagocytosis, photoreceptor loss occurs subsequently. Although the most remarkable clinical and pathological injuries are present in RPE and its underlying Bruch’s membrane, it is the structural and functional disruptions, even the death of photoreceptors, via either atrophic or neovascular process, that take responsibilities for the visual impairment in AMD. Furthermore, the condition of photoreceptors directly reflects the significance of lesions in RPE/Bruch’s membrane complex.
\nIt is crucial to identify which type of photoreceptors are most severely damaged in AMD, not only for the potential therapeutic strategies targeting the most affected cells, but also for investigation of mechanism of these pathological changes. The rate of rod and cone degeneration is representative in different situations affecting photoreceptors. In aging retina without age-related maculopathy, the number of cells in the cone-dominated fovea remained stable, while the number of rods in the parafovea was reduced by 30% [49]. In both non-exudative and exudative forms of AMD, photoreceptors were lost. In addition, more rod loss was observed than that of cones; gradually, only degenerated cones were left; finally, all photoreceptors might die [50]. The pathological changes mentioned above were consistent with the functional research exhibiting that scotopic sensitivity decreased more than photopic sensitivity in cases with AMD [51]. Maeda and colleagues revealed the apoptosis of photoreceptors after RPE damage caused by intravitreal injection of ornithine in rats, indicating the important role of RPE cells in maintaining photoreceptor integrity [52]. In AMD patients, apoptotic photoreceptors and RPE cells (TUNEL positive) were observed as well. Most of TUNEL-positive photoreceptors were rods and located at the edge of RPE atrophy. Moreover, Fas was upregulated in apoptotic photoreceptors, indicating Fas/FasL might be involved in the apoptosis process [53]. Studies performed by Kim and collaborators demonstrated that significant reduction of photoreceptors was shown in the areas where RPE cells were totally lost in GA, and where disciform scar formed in wet AMD [54, 55]. In terms of disciform scar, thickness of the scar was closely associated with photoreceptor loss, which means that the thicker the disciform scar, the less photoreceptors survived [56].
\nIn addition to photoreceptors, other retinal neurons are also affected. Joshua et al. first reported that TUNEL-positive cells were detected in the inner side of INL, indicating that these cells might be amacrine cells [53]. In cases with GA, retinal ganglion cells were significantly decreased by 30.7% compared to age-matched control. However, cell nuclei in INL were not significantly different [54]. In wet form of AMD, a decrease of ganglion cells and increase of cells in INL were observed, but there was no significant difference [55].
\nSeveral treatments have been adopted for AMD management. However, current therapeutics can only slow the progression of the disease, trying to delay the onset of vision loss as much as possible.
\nTo date, there are no approved drugs for the dry form of AMD. Therefore, much effort has been made to reduce risk factors. Among various risk factors, oxidative stress induced by inflammation, light exposure, and so on is considered as one of the most important risk factors for the occurrence and progression of AMD. Numerous studies have been conducted to evaluate the relationship between antioxidant nutrition supplementation and AMD. In the Age-Related Eye Disease Study (AREDS), it was demonstrated that oral dietary supplementation containing vitamin C (500 mg/day) and E (400 IU/day), zinc oxide (80 mg/day), cupric oxide (2 mg/day), and beta-carotene (15 mg/day) can decrease the risk of development of AMD from intermediate stage to advanced stage [11]. Since beta-carotene was observed to increase the risk of lung cancer in cigarette smokers, and 80 mg/day zinc is out of tolerance for individuals, there were elimination of beta-carotene, decreased dose of zinc (25 mg/day), and adding of lutein and zeaxanthin in the AREDS2 formula [12, 57, 58]. A 10% reduction in developing to advanced AMD was present in patients treated with AREDS2 formula containing lutein and zeaxanthin [59]. In addition, healthy diet rich in fish, green leafy vegetables, and nuts together with healthy lifestyle are strongly recommended to reduce AMD risk factors [15].
\nFor the wet form of AMD, therapies mainly focus on halting the progression of CNV. Thermal laser photocoagulation is the first treatment to stop the progression of CNV successfully, but with no significant vision improvement and high recurrence of CNV. Photodynamic therapy with verteporfin can selectively damage the CNV tissue without additional injuries of neighboring tissue, but this therapy has no effects on visual improvement either. In terms of upregulation of VEGF in the development of CNV, intravitreal injection of anti-VEGF drugs has been widely used by ophthalmologists as a standard treatment. Anti-VEGF drugs (Pegaptanib sodium, Ranibizumab, and Bevacizumab) for exudative AMD have demonstrated exciting results: the vision in the majority of patients remained stable for 1 year, of which 40% of patients had visual improvement. Surgical intervention to remove CNV and submacular hemorrhage did not improve VA, which is the result of recurrence of CNV [15, 60].
\nNeuroprotection comprises a large number of therapeutic interventions to improve survival of neurons by modifying the structure and function of neurons, and/or their microenvironment. Initially, neuroprotective therapies are focused on central nervous system diseases including stroke, Alzheimer’s disease, Parkinson’s disease, etc. Since retina is regarded as the end part of central nervous system, a series of neuroprotective strategies has been applied to prevent vision loss by protecting retinal neurons. Furthermore, tremendous neuroprotective strategies are under investigation in both experimental and clinical research.
\nNeurotrophic factors, belonging to the family of growth factors, have the ability to promote survival of retinal neurons. Ciliary neurotrophic factor (CNTF) is one of the most extensively studied neurotrophic factors for neural retina protection. La Vail and collaborators first reported that intraocular injection of CNTF obviously prevented photoreceptor death from light-induced damage in Sprague Dawley rats [61]. Subsequently, intravitreal injection of adenoviral vector containing CNTF cDNA in rd1 mice, a naturally occurring mouse model for retinal degenerative diseases, demonstrated the reduction of photoreceptor loss, conservation of ONL thickness, and increase of photoreceptor segments’ length. Moreover, the amplitudes of a-wave and b-wave in electroretinogram (ERG) were significantly increased compared with those of the control group, suggesting the preservation of retinal functions [62, 63]. Later, long-term protective effects of photoreceptors were shown using adeno-associated virus to deliver CNTF to the retina [64]. In order to sustainably deliver neurotrophic factors, encapsulated human RPE cells secreting CNTF were transplanted into the vitreous of rcd1 dog (a dog model of retinal degeneration). A significant increase of ONL thickness was observed in the treated eye as a result of continuous release of CNTF at the nanogram level [65]. No adverse effects were exhibited in the retina of transplanted eyes during the whole experiment period (7 weeks). In addition, CNTF was proved to protect loss of cone outer segments, an early sign of cone degeneration, indicating that CNTF could not only slow or halt progression of degeneration but also might reverse degeneration [66].
\nPlacental growth factor (PIGF), one of the members of vascular endothelial growth factor family, was believed to prevent neuronal injury in the brain. In the retina, the role of PIGF was exhibited quite differently in in vitro and in vivo studies. Blue light-induced murine photoreceptor cell death was significantly attenuated after the treatment of PIGF by suppressing caspase-3/7 activity through the mitogen-activated protein kinase (MEK) and phosphoinositide 3-kinase (PI3K) pathway. Anti-PIGF antibody eliminated these protective effects [67]. However, in the light-induced retina-damaged mouse model, PIGF induced decreased ONL thickness and dysfunction of retina. Anti-PIGF antibody diminished neuroretinal injury and disruption of RPE cell-cell junctions after exposure to the white light for 3 h [68]. The opposite effects of PIGF and its antibody in mice were later found to be due to the hyperpermeability of RPE induced by PIGF, leading to the breakdown of retina-blood barrier and subsequent damages [68].
\nUrsodeoxycholic acid (UDCA) and its taurine-conjugated derivative tauroursodeoxycholic acid (TUDCA) were first found in the bile acid of hibernating bears. They have been used for liver detoxification, dissolution of gallstone and kidney stone, suppression of convulsions, and visual improvement in traditional Chinese medicine for a very long time. According to the theory of modern medicine, UDCA and TUDCA exhibit neuroprotective effects through prevention of cell apoptosis [69]. TUDCA treatment significantly preserved the number and structure of photoreceptors and retinal functions in different murine models of photoreceptor degeneration, including rd10 mice, rd1 mice, BALB/c mice, Bardet-Biedl syndrome type 1 mice, and transgenic P23H rats [69]. Furthermore, TUDCA manifested greater protective effects in cones [70]. In vitro studies using photoreceptor 661 W cells revealed that reduced endoplasmic reticulum (ER) stress and improved trafficking of cyclic nucleotide-gated channels in cones contributed to neuroprotective effects of TUDCA [71]. In addition, TUDCA improved phagocytosis of POS in H2O2-treated RPE cells via activating Mer tyrosine kinase receptor (MerTK), which indirectly protected photoreceptors [72].
\nEndogenous and exogenous progesterone have been certified to have neuroprotective effects in brain and retina for several decades. A broad range of studies have been conducted in either light-damaged or genetic murine models of retinal degeneration, demonstrating improvement in photoreceptor survival, decreased gliosis, and reduced retinal dysfunction after administration of progesterone or synthetic progestins [69]. A group of researchers in Spain revealed that rd1 mice orally administered with progesterone (100 mg/kg body weight) at postnatal day 7 (P7) exhibited significantly decreased number of apoptotic cells in ONL in the far peripheral retina and increased amplitude of ERG b-wave at P15, but no significant change was observed at P17. There was also a transient reduced gliosis in the treated rd1 mice [73]. Similar results were observed with oral administration of synthetic progestin, known as the FDA-approved Norgestrel, showing reduction of photoreceptor death by 70 and 75% in light-damaged mouse model and rd10 mice, respectively [74]. The rescue effects were achieved by increasing production of basic fibroblast growth factor and its downstream pro-survival reactive oxygen species [74, 75].
\nCrystallins, critical family members of small heat shock proteins, have been identified to have novel functions in both retina and RPE as in the lens, such as anti-apoptosis and anti-inflammation. αB-Crystallin is secreted in exosomes released from apical surface of polarized RPE cells and accumulates in the matrix among photoreceptors, and therefore may protect neighboring RPE and photoreceptors [76]. RPE cells in αB-crystallin knockout mice exhibited high susceptibility to oxidative stress and endoplasmic reticulum stress compared to the RPE cells from wild-type mice. Furthermore, RPE cells overexpressing αB-crystallin were more resistant to apoptosis, indicating the protective effects of αB-crystallin [77]. In the mouse model of AMD induced by sodium iodate (NaIO3), absence of αB-crystallin accelerated RPE apoptosis with subsequent death of photoreceptors through upregulation of AKT phosphorylation and expression of peroxisome proliferator-activator receptor-γ, suggesting αB-crystallin, especially the small peptide called mini cry, may play an important role in the protection of retinal degeneration [77]. In order to prolong the life of mini cry in the vitreous, free mini cry was fused to form an elastin-like polypeptide (ELP), which could be detected in the vitreous for up to 2 weeks. One intravitreal injection of ELP-linked peptide prevented RPE cells from apoptosis, inhibited activation of caspase-3 activation, and protected neural retina for up to 1 month after NaIO3 challenge [78].
\nNF-E2-related factor 2 (Nrf2) is a transcription factor that regulates antioxidant responses in many tissues and cell types, providing protection against oxidative stress. Under the oxidative stress, Nrf2 is translocated from cytoplasm to nucleus, and subsequently binds to the corresponding sites to activate transcription of a wide range of antioxidant genes. In the central nervous system, Nrf2 was proved to slow the neurodegeneration by means of antioxidative stress and neuroinflammation [79]. In the mice undergoing optic nerve crush (an animal model of glaucoma), retinal ganglion cells were significantly decreased than in the wild-type mice. With the treatment of Nrf2 activator, retinal ganglion cell loss was decreased by upregulating gene expression of antioxidant and phase II detoxifying enzymes [80]. After retinal ischemia-reperfusion injury, Nrf2 knockout mice showed greater loss of retinal ganglion cells when compared with the wild-type mice. Moreover, after ischemia-reperfusion injury, Nrf2 activator increased survival of retinal ganglion cells in wild-type mice, but not Nrf2 knockout mice, indicating the neuroprotective effects of Nrf2 [81]. An in vitro study has demonstrated that siRNA knockdown of Nrf2 led to significant increase of reactive oxygen species and cell death after blue light exposure in murine photoreceptor cells, suggesting that Nrf2 could be used to protect photoreceptors in AMD and other retinal degeneration [82].
\nThere is no direct evidence that dopamine protects retinal neurons through anti-inflammation, apoptosis, and oxidative stress. However, decreased expression of dopamine may play a negative role in neuron survival in the brain [83]. Thus, dopamine receptor agonists may achieve the goal to promote retinal neuron survival and function by restoring the lost dopamine resulting from certain diseases. Dopamine receptor agonist pramipexole was shown to ameliorate structural and functional injuries in the light-damaged mice, exhibiting decreased photoreceptor death, damage of photoreceptor outer and inner segments, TUNEL-positive cells in ONL, and preservation of a-wave and b-wave in ERG [84]. Additionally, pramipexole inhibited ARPE-19 cell (an immortalized RPE cell line) death after H2O2 treatment, suggesting its antioxidative effects [84].
\nRegular general physical exercise is not only a part of healthy life style, but also a rehabilitation strategy showing neuroprotective effects in numerous diseases. Recently, exercise has been proven to be neuroprotective in animal models of retinal degeneration [85, 86, 87]. Wild-type BALB/c mice were forced to exercise for 5 days/week for 2 weeks before being exposed to bright light. Exercised mice showed greater improved amplitude of ERG b-wave and photoreceptor nuclei than the mice without exercise after light exposure [85]. In order to exclude the impact of stress caused by forced exercise, voluntary wheel running was adopted in rd10 mice, which also demonstrated protection of VA, and the number of cones and total photoreceptors [86]. No matter if it was involuntary or voluntary exercise, either one benefited damaged photoreceptors in animal models of retinal degeneration through increased expression of brain-derived neurotrophic factor (BDNF) and activation of tropomyosin-related kinase B (TrkB) signaling pathway [85, 86].
\nElectrical neurostimulation has developed rapidly in recent years, covering a range of neurological diseases, such as neurostimulation for epilepsy, spinal cord stimulation for chronic pain, brain stimulation for Parkinson’s disease, and so on [88]. Electrical neurostimulation in vision research has also made a great improvement, from transcorneal, subretinal, to whole-eye electrical stimulation. Transcorneal electrical stimulation (TES) was performed in SD rats before or after exposure to the intense light for 14 days. Both the stimulation before and after light exposure slowed the progression of photoreceptor degeneration. Furthermore, TES after light exposure exhibited a longer and better protective effect. The neuroprotection effects may result from anti-apoptosis (upregulation of Bcl-2 and downregulation of Bax) and increased expression of neurotrophic factors (CNTF and BDNF) [89]. Subretinal electrical stimulation (SES) in the eye of Royal College of Surgeons (RCS) rats, a commonly used model of retinal degeneration, significantly preserved amplitudes of b-wave and oscillatory potential, and implicit times of a-wave and b-wave, suggesting the preservation of not only photoreceptors, but also signal transmission in the retina [90]. Although the whole-eye electrical stimulation did not provide protection for rod structure in P23H rats, b-wave amplitudes and rod sensitivity were significantly increased [91].
\nAlthough the neuroprotective strategies mentioned above have acquired promising results in animal models, damaged human retina can be only partially rescued. Miller and colleagues have investigated the underlying reasons using many animal models. There are three types of cell death including caspase-mediated apoptosis, autophagy-mediated cell death, and necrosis which is regulated by receptor-interacting protein kinases (RIPK). In the animal model of retinal detachment, elevated expression and phosphorylation of RIPK together with activation of caspases were observed. When either RIPK or caspases were inhibited, no obvious rescue effects were demonstrated. However, inhibition of both RIPK and caspases resulted in significant protective effects. Similar results were found in the animal model of retinal degeneration which experienced both photoreceptor and RPE cell death. In this animal model, the dominant type of cell death in photoreceptors was apoptosis, while necrotic RPE cells were mainly exhibited. Therefore, therapies that block both RIPK and caspase pathways may provide more satisfactory neuroprotection [92].
\nNeurotech Pharmaceuticals developed an intraocular drug delivery system using encapsulated cell technology, called NT-501, to consistently release CNTF in the vitreous for more than 2 years [93]. A double-masked, randomized, sham-controlled, phase II study enrolling 51 GA patients who were randomly divided into 3 groups, high-dose NT-501 implant, low-dose NT-501 implant, and sham control group, demonstrated promising outcomes after a 2-year evaluation [94]. Another phase II study using intraocular implant secreting CNTF in GA patients has also been completed. In this multicenter, double-masked, randomized, sham-controlled, 1-year, dose-ranging phase II study, VA stabilization, defined as loss of less than 15 letters, was observed in the high-dose group (96.3%) versus low-dose group (83.3%) and sham surgery (75%). All the patients with best corrected VA ≥20/63 in the high-dose group lost less than 15 letters, while it was only 55.6% in the combined group of patients treated with low-dose implant and sham surgery. Additionally, increase of retinal thickness was consistent with the stabilization of VA [95].
\nBrimonidine, an α-2 agonist, is usually used to treat glaucoma patients in ophthalmology. Since brimonidine has been reported to protect neuroretinal cells in murine models, the effects of this drug on dry AMD patients were under evaluation [96]. A randomized, double-masked, sham-controlled, phase II study involved 119 patients with bilateral GA who were randomly divided into 3 groups including 200 μg treatment, 400 μg treatment, and sham control group. The efficacy and safety of brimonidine on biodegradable implant were evaluated after intravitreal transplantation [97]. But the results were not reliable, so another multicenter study is currently performed with larger samples (311 eyes receiving either treatment of 400 μg brimonidine on biodegradable implant or sham treatment) and longer evaluation period (up to 24 months) [98].
\nA retrospective analysis was performed to analyze the association between intake of
There are few studies about exercise as a clinical intervention in AMD, and the published papers are observational research showing the correlation between exercise and prevalence of AMD. A study lasting for 15 years demonstrated that active lifestyle with physical exercise 3 times per week or more was associated with reduced risk to develop wet AMD [101]. In another cohort study, an inverse relation was observed between vigorous exercise (≥3 times/week) and occurrence of intermediate AMD in women, not in men [102]. In addition, low physical exercise was related to the formation of drusen larger than 63 μm [103].
\nElectrical neurostimulation has already been applied to patients using less invasive approaches, such as transcorneal or whole-eye electrical stimulation, to protect the structure and function of retinal neurons. In a clinical trial conducted by Anastassiou and colleagues, 22 patients with dry AMD received transpalpebral electrical stimulation twice a day for 5 days. Most of the patients demonstrated the improvement in VA and contrast sensitivity at 4 weeks after the treatment; however, only contrast sensitivity was significantly different when compared with sham control group [104]. Similarly, microcurrent stimulation (150 μA for 35 min) once a week for 3 months was applied to both dry and wet AMD. Significantly increased VA was demonstrated in dry AMD, but not in wet AMD. Moreover, the number of patients showing increased VA was twice as those exhibiting deterioration [105].
\nAMD is believed to have stronger relationship with age. With increase of aging population worldwide, more individuals are suffering from visual damage, resulting in poor quality of life for the aged people and elevated cost of medical care. In AMD, it is not only the degeneration of RPE cells, but also the neural retina degeneration, especially photoreceptors, that leads to the visual impairment. Therefore, neuroprotection can be one of the therapeutic strategies to slow, halt, or even reverse the progression of retina degeneration. Although the neuroprotective interventions that are currently investigated in both animal models and patients demonstrate promising results, it is of importance to identify the long-term efficacy and safety of these interventions. Only after that, the therapeutics will be provided to patients to help them maintain vision and further improve the quality of life.
\nThis work is supported by the Project of Dominant Discipline Construction in Universities of Shaanxi Province-Basic Medicine, Shaanxi Province, China, awarded to Xi’an Medical University as well as Health and Medical Research Fund, Hong Kong (04150746), awarded to Amy C.Y. Lo.
\nThe authors declare no conflict of interest.
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\\n\\nIntechOpen’s Academic Editors and Authors are members of our growing scientific community focused on quality, peer-reviewed research and expeditious spreading of knowledge. Our community comprises of senior members of the international academic and scientific community across all fields of science, whose merits include Nobel Laureates and the world's top 1% most cited authors.
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