## 1. Introduction

Filter banks decompose signals into multiple sub-bands to perform various processes and reconstruct the original signals [1]-[8]. It has been demonstrated that sub-band processing with filter banks improves the performance of numerous image processing applications such as image recognition, watermarking, image coding,and so on [9]-[14].

For example, image recognition accuracy is improved with sub-band processing. A watermark is typically embedded into a middle frequency band in order to make the scheme not only robust to compression but also secure against attacks on the watermark. Thus, it is highly desirable that analysis filters have excellent frequency characteristics to appropriately decompose signals in such applications.

In sub-band coding, on the other hand, we usually focus more on various visual issues proper to compression such as checkerboard artifacts and blocking effects, rather than the frequency characteristics of the analysis filters. Coding gain is also one of the significant issues in image coding. Therefore, various constraints are imposed on the filters to deal with such problems in the design. Accordingly, the design is usually performed with non-linear optimization, which increases its design complexity.

Multi-resolution processing, which is an extension of sub-band processing, is also in demand in a number of applications. Multi-resolution schemes such as wavelet transform not only decompose the frequencies of signals but also yield various lower resolution versions of the original signals by gradually reducing the sampling rate with filter banks.

Meanwhile, image scaling has become highly required processing in the context of the diversification of the means of displaying images [15]-[17]. Image scaling changes the resolution of images more flexibly than multi-resolution processing, although it does not decompose the frequency components of signals. Typically it consists of up/down samplers and a filter which suppresses the imaging or aliasing components caused by the samplers.

Under the circumstances, we discuss complex processes where sub-band processing and scaling are carried out sequentially in this chapter. For instance, when watermarked images are scaled, or scaling is applied to decoded images, a filter bank and a scaling structure are designed separately and implemented sequentially. However, they have the structural similarity in that they involve samplers and filters. This implies that one can integrate them in order to implement them with lower implementation cost.

Therefore, this chapter provides a profound discussion on a filter bank structure to yield an effective scheme in computation in such complex processing. We introduce a simple filter bank structure for computational simplicity which directly synthesizes the analysis decomposed signals to produce a scaled signal, not to reconstruct the original signal as in traditional perfect reconstruction filter banks.

First, we discuss the frequency decomposition characteristics of the filter bank to show how to obtain arbitrarily scaled signals. We briefly clarified the band-width of the synthesis filters to obtain the scaled signal from the analysis part of the filter bank in [18]. However, the discussion is not sufficient since the behavior of the aliasing components, which can significantly affect the filter bank performance, is not clarified. This chapter discusses the issue so that the feature of the filter bank will be manifested. It is shown that the aliasing problem is similar to that of perfect reconstruction filter banks.

Next, theoretical conditions for addressing the above aliasing problem are given through the input-output relation of the filter bank. We also discussed the strategy to make the filter bank equivalent to the direct scaling structure by deriving such theoretical conditions in [18]. However, we observed the number of conditions is proportional to that of aliasing components, which can lead to time-consuming design or complicated optimization. Moreover, we also know the conditions are usually not satisfied when all the filters have linear phase, so that the derived filter banks do not have excellent performance.

In this chapter, we discuss the conditions from a viewpoint of the aliasing components to verify that they are the same regardless of the aliasing components, so that the number of equations to solve is significantly reduced. A design procedure with the conditions is also discussed. We mention that the procedure is applicable to various image processing mentioned above. In addition, it is demonstrated through simulation results that scaled images' quality is comparable to that of the sequential structure even though not all the filters have linear phase characteristics. Finally, we discuss potential issues and advantages in making use of the scheme as well as traditional ones in practical image processing.

## 2. Image scaling

Image-scaling which changes the resolution of images has become highly required processing in the context of the diversification of the means of displaying images. In such context, various image processing such as image coding, watermarking and recognition is also applied before image scaling, in which a filter bank is often accompanied by a scaling structure sequentially in practice since filter banks are used in many image processing applications with their sub-band decomposition performance.

Typically, the direct scaling structure consists of up/down samplers and a filter which suppresses the imaging or aliasing components caused by the samplers. Similarly, the filter bank structure also involves the samplers and filters. This indicates that one can integrate both the structure in order to implement them with lower computational complexity than that of their sequential structure shown in Figure 1.

For this, we provide a study on a simple filter bank structure depicted in Figure 2 which is a simplified version of the structure given in [18]. Please note that the sampling rate at the output in both the structures Figure 1 and Figure 2 is the same when

The aim of this chapter is to demonstrate that the computational complexity of Figure 2 for scaling is reduced compared to that of Figure 1, while its scaling performance is comparable to that of Figure 1.

## 3. A discussion on the computational complexity of the filter bank for scaling

First, we discuss the computation cost of the introduced filter bank drawn in Figure 2 as well as the sequential structure shown in Figure 1.

The sequential structure decomposes the input signal into sub-band frequency components first with down-sampling by the factor

On the other hand, the introduced filter bank structure also decomposes the input into multiple sub-band signals with down-sampling by

Accordingly, we see that the sequential structure requires two down-sampling and up-sampling processes, while the introduced filter bank structure is carried out with only one down-sampling and up-sampling process. Such a structural difference can cause different computation time for scaling signals.

If we assume that both the systems have the same length of the analysis filters, then the difference in length between both the systems will be in their synthesis part. The difference will also cause different computation time. We also discuss these concerns with the design and simulation in both of the systems in detail.

## 4. A discussion on the filter bank structure for scaling

### 4.1. The frequency decomposition and synthesis characteristics of the filter bank

Next, we discuss the performance of the frequency decomposition and synthesis with the filter bank. Figure 3(a) shows an original signal

The analysis signals such as Figure 3(b) and (c) are described in the z domain as

where

These analysis signals are up-sampled by the factor

These signals in the synthesis part are represented as

The eventual scaled signal, which is the output of the filter bank, is drawn in the Figure 3(f), and also represented as

In this manner, one can obtain a scaled signal by using the filter bank, and the scaled signal is completely reconstructed when the ideal filters are used in the filter bank. However, it is not possible to use such filters, as in traditional filter banks, and the aliasing components affect the scaling performance with the filter bank.

Therefore, we provide a discussion on the aliasing components in the next, which manifests the scaling behavior of the filter bank.

### 4.2. The aliasing components of the filter bank

The output signal of the filter bank is rewritten as

When

On the other hand, when

However, it can also be verified in the frequency domain that such serious aliasing is essential and necessary to obtain a scaled signal, since the aliasing components themselves form the scaled signal in part in thiscase.

In this sense, one can conclude that even though the input signal is scaled, only minor aliasing between the aliasing components caused by the non-ideal filters is taken into account to design the filter banks as well as the traditional filter banks.

We also compare the direct scaling structure in the sequential structure shown in Figure 1 with the filter bank of Figure 2 in terms of aliasing. In the direct scaling structure, up-sampling by the factor of

On the other hand, the filter bank structure performs down-sampling by

These are possible issues to address when we design the filter banks that serve as scaling systems.

## 5. The theoretical conditions for the improvement of the scaling performance

Typically, traditional perfect reconstruction filter banks are designed to satisfy perfect reconstruction conditions completely or approximately so that aliasing will be eliminated. Similarly, we provide theoretical conditions for the introduced filter banks to reduce aliasing.

We see, from Figure 3 and 4, that aliasing in Figure 3 is reduced if the eventual signal shown in Figure 3(f) is the same as Figure 4(c). In other words, it is desired that the introduced filter banks precisely work as the direct scaling structures. Therefore, a design scheme for the filter banks that approximately or exactly serve as the direct scaling structures is provided in this section.

### 5.1. The input-output relation in the filter banks and the direct scaling structures in the frequency domain

First, we give the relation between the input and output of the filter banks and the direct scaling structures. The output signal of the direct scaling structure in the sequential structure shown in Figure 1 is written as

Please note that we usually use a perfect reconstruction filter bank in Figure 1, therefore, it is assumed that the output of the filter bank is the same as its input.

On the other hand, the output signal

If the signal

in which

hold in some cases without any manipulation, or holds in the other cases by changing the order of

### 5.2. A discussion on the conditions

First, we discuss the equations (8) to verify that they hold. We assume that

hold, the equations

need to hold in order to satisfy the equations (8). n is an arbitrary integer number.

If

If we replace

then we also obtain

hold. We see that the equations (14) also hold when

Hence, the equations

need to hold in order to satisfy the conditions (7).

Next, we verify the equations (15) are the same regardless of

(16) |

where

Here we define the following vector

in which

Moreover, the

If we denote

On the other hand, we also define the following vector

where the

However, it is observed that equations are usually not satisfied when all the filters have linear phase characteristics. Accordingly, we do not impose the symmetry constraints on all the filters. We also see that the filter in the desired direct scaling structure needs to have some delays in order to satisfy the equations. The performance of such filter banks is examined in the next section.

## 6. Design and Simulation Results

The equations derived in the previous section have unknown parameters

The concrete design procedure depends on applications the filter banks are utilized in. For example, several constraints should be imposed on the analysis filters

The analysis filters should have high attenuation in the stop band, or excellent frequency decomposition performance, when the filter banks are employed for watermarking, since the watermarks are often embedded only into one narrow sub-band that is robust to attacks.

These tell that the analysis filters typically suffer from severe constraints. In this section, we survey the case where the analysis and synthesis filters are separately designed, i.e., only

With this design policy, we here design filter banks. As an example, two-channel and three-channel filter banks are designed and explored. In the two-channel case, the scaling factor is 3/2, and in the three channel one, it is 2/3. The length of the linear phase analysis filters is 6 in the two-channel case, and 8 in the three-channel one. The length of the obtained (non-linear) synthesis filters is 9, and 6, respectively. The length of the linear phase filter in the direct scaling structure is 3, and 2, respectively, and it has third and half band amplitude characteristics, respectively. This is summarized in Table1.

Please note that in the introduced filter bank structures, the direct scaling filters are not required for scaling since the filter banks perform not only sub-band processing but also scaling. The direct scaling filters are used only to design the filter banks. The round brackets “(· )” are used in this sense in Table 1. It is observed that the direct scaling filters have some delays so that the conditions will be satisfied.

The number of channels | The scaling Factors | The length of the analysis, synthesis and direct scaling filters | ||

2 | 3/2 | 6 | 9 | (3) |

3 | 2/3 | 8 | 6 | (2) |

For comparison, we also design nearly perfect reconstruction filter banks in the two-channel and three-channel case, where the length of the analysis filters is 6, and 8, respectively. Please note that the sequential structure consists of the perfect reconstruction filter bank and the same direct scaling structure (except the delays). This is also shown in Table2.In the sequential structures, the coefficients of the analysis filters partially have a few zero-values to satisfy the perfect reconstruction conditions. The sequential structures also require direct scaling filters to perform the scaling process.

The number of channels | The scaling Factors | The length of the analysis, synthesis and direct scaling filters | ||

2 | 3/2 | 6 | 6 | 3 |

3 | 2/3 | 8 | 9 | 2 |

The elapsed computation time required for signal scaling in both the sequential structure and the introduced filter bank is also compared. The computation is carried out on a computer with a 2.2 GHz processor. In this comparison, we examine the elapsed time with various scaling factors and length of the filters.

For example, the elapsed time is 0.2030, and 0.2660 in the introduced structure and the sequential one designed above, respectively, when

In addition, Table 3 shows the elapsed time[s] when

It is observed that the elapsed time is proportional to the factor
*U* = 9, 11. However, this drawback is improved if each branch of the filter banks is completely carried out in parallel, i.e., the computation time of the filter banks is the same as that of each branch in the filter banks. We also explored the elapsed time of both systems in this case, which is also shown in the round brackets “(· )”, where the length of the analysis filters is also 30, in Table 3.

We also explore the performance of image-scaling in the two-channel and three-channel cases. Several well-known test images (

The number of channels | LENA | BARBARA | BOAT | Building | Lighthouse |

2 | 147.60 | 148.88 | 145.40 | 146.64 | 146.56 |

3 | 122.80 | 123.96 | 121.02 | 122.12 | 121.82 |

The introduced filter banks as well as the sequential structures can be applicable to practical image processing applications. In this simulation, we examine the performance of both the systems in watermarking, where it is assumed that the watermark is embedded into a sub-band of the original image and then the watermarked image is rescaled, and that the watermark is extracted from the rescaled image subsequently. We illustrate both the systems in watermarking with Figure 5 and Figure 6 in which the scaling factor is 3/2.

Please notice that the subsequent extracting stage in both the systems requires down-sampling and also the use of a scaled version of the original image, in order to extract the watermark embedded in a sub-band of the original image in this simulation. The only difference between both of the systems is that the sub-band signals are synthesized and then rescaled sequentially in Figure 5 while they are rescaled in each sub-band and synthesized in Figure 6. This also holds for other image processing applications such as image coding, recognition, etc.

To demonstrate this, we use several original test images (

Table 5 represents the PSNR[dB] of the extracted watermark against the original watermark in both the systemsin which the original image “LENA” is used. It is observed that the obtained watermarks with the introduced filter banks are comparable to those of the sequential structures, while the computation time of the proposed filter bank is less than that of the sequential structures as shown above.

Watermark1 | Watermark2 | ||

Proposed structure | Sequential structure | Proposed structure | Sequential structure |

34.38 | 34.56 | 34.66 | 34.88 |

We, however, see that the water marks are not extracted with very high PSNR in both the systems, which is because re-sampling or filtering is carried out in the image-scaling and watermark-extracting stage.

Typically, watermarks can also be deteriorated in quality or eliminated by attacks such as filtering, scaling, etc. Thus, they are more sensitive to such attacks than usual watermarking (without scaling) is. A detailed discussion is also the future work.

## 7. Conclusion

This chapter has discussed a filter bank structure which directly synthesizes the analysis decomposed signals to produce a scaled signal, which leads to computational simplicity in complex processing such as sub-band processing followed by scaling.

First, we have discussed the frequency decomposition characteristics of the filter bank to show how to obtain arbitrarily scaled signals, where the behavior of the aliasing components has also been manifested and addressed, since it can significantly affect the filter bank performance as in traditional perfect reconstruction filter banks.

Next, theoretical conditions for designing the filter bank that is equivalent to the direct scaling structure have been given with the input-output relation of the filter bank, where it has been mentioned that the number of conditions is proportional to that of aliasing components, and that the conditions are mostly not satisfied when all the filters have linear phase. Therefore, we have discussed the conditions, so that they are the same regardless of the aliasing components, which means that the number of equations to solve is significantly reduced.

In addition, it has been demonstrated through simulation results that the computation time required for signal scaling with the proposed scheme is usually less than that of the sequential structures, and that the quality of scaled images is comparable to that of the sequential structures even though not all the filters have linear phase characteristics.

Finally, we have discussed potential issues and advantages in employing the proposed scheme as well as traditional ones in practical image processing such as watermarking.

The findings in this research are that the derived solution by using the proposed approach yields comparable scaling and watermarking performance to that of the usual sequential structures that consist of filter banks and also the direct scaling processes, while the computation time required for scaling is less than that of the sequential scheme and the design procedure is considerabley simplified.