Sergey Abramov

By Sergey Abramov, Victoria Zabrodina, Vladimir Lukin, Benoit Vozel, Kacem Chehdi and Jaakko Astola

Part of the book: Numerical Analysis

By Sergey Abramov, Victoriya Abramova, Vladimir Lukin, Nikolay Ponomarenko, Benoit Vozel, Kacem Chehdi, Karen Egiazarian and Jaakko Astola

Part of the book: Computational and Numerical Simulations

By Benoit Vozel, Oleksiy Rubel, Alexander Zemliachenko, Sergey Abramov, Sergey Krivenko, Ruslan Kozhemiakin, Vladimir Lukin and Kacem Chehdi

In processing of multichannel remote sensing data, there is a need in automation of basic operations as filtering and compression. Automation presumes undertaking a decision on expedience of image filtering. Automation also deals with obtaining of information based on which certain decisions can be undertaken or parameters of processing algorithms can be chosen. For the considered operations of denoising and lossy compression, it is shown that their basic performance characteristics can be quite easily predicted based on easily calculated local statistics in discrete cosine transform (DCT) domain. The described methodology of prediction is shown to be general and applicable to different types of noise under condition that its basic characteristics are known in advance or pre-estimated accurately.

Part of the book: Environmental Applications of Remote Sensing

By Vladimir Lukin, Alexander Zemliachenko, Ruslan Kozhemiakin, Sergey Abramov, Mikhail Uss, Victoriya Abramova, Nikolay Ponomarenko, Benoit Vozel and Kacem Chehdi

In this chapter, we consider lossy compression of multichannel images acquired by remote sensing systems. Two main features of such data are taken into account. First, images contain inherent noise that can be of different intensity and type. Second, there can be essential correlation between component images. These features can be exploited in 3D compression that is demonstrated to be more efficient than component-wise compression. The benefits are in considerably higher compression ratio attained for the same or even less distortions introduced. It is shown that important performance parameters of lossy compression can be rather easily and accurately predicted.

Part of the book: Recent Advances in Image and Video Coding