Beyond Traditional Transform Coding

Beyond Traditional Transform Coding

Wavelet, Subband and Block Transforms in Communications and Multimedia

Wavelet and subband transforms have been of great interest in the fields of - gineering and applied mathematics. The theories of these powerful signal p- cessing tools have matured and many applications utilizing them are emerging in different disciplines. This book, comprised of eleven chapter contributions from prominent researchers in the field, focuses on communications and mul- media applications of wavelet and subband transforms. The first six chapters of this book deal with a variety of communications applications that significantly benefit from wavelet and subband theories. S- ilarly, the remaining five chapters present recent advances in multimedia - plications of wavelet and subband transforms. These chapters interconnect the requirements of applications with the underlying theory and their engineering solutions. Hence, the reader can easily trace the entire path from fundamentals to the purpose and merit of application in hand. A combined list of references for the entire volume is given at the end of the text that should be helpful to the interested reader for a further study. This book is anticipated to be of particular interest to engineers and sci- tists who want to learn about state-of-the-art subband and wavelet transform applications as well as their theoretical underpinnings. It can also serve as a supplementary book for graduate level engineering and applied mathematics courses on wavelet and subband transforms.

Graph Spectral Image Processing

Graph spectral image processing is the study of imaging data from a graph frequency perspective. Modern image sensors capture a wide range of visual data including high spatial resolution/high bit-depth 2D images and videos, hyperspectral images, light field images and 3D point clouds. The field of graph signal processing – extending traditional Fourier analysis tools such as transforms and wavelets to handle data on irregular graph kernels – provides new flexible computational tools to analyze and process these varied types of imaging data. Recent methods combine graph signal processing ideas with deep neural network architectures for enhanced performances, with robustness and smaller memory requirements. The book is divided into two parts. The first is centered on the fundamentals of graph signal processing theories, including graph filtering, graph learning and graph neural networks. The second part details several imaging applications using graph signal processing tools, including image and video compression, 3D image compression, image restoration, point cloud processing, image segmentation and image classification, as well as the use of graph neural networks for image processing.