This book provides a theoretical and technical basis for advanced research on visual data compression and communication. It presents the results of the author's research on visual data compression and transmission. Studying scalable video coding (SVC), it considers the fundamental problem to be solved in SVC-motion compensation. It explores directional transforms, extends the current coding framework by visual synthesis and reconstruction, and explains how to apply compressive sensing to solve the compression problems in transmission. It also develops the pseudo-analog transmission for image and video.
The communication chain is constituted by a source and a recipient, separated by a transmission channel which may represent a portion of cable, an optical fiber, a radio channel, or a satellite link. Whatever the channel, the processing blocks implemented in the communication chain have the same foundation. This book aims to itemize. In this first volume, after having presented the base of the information theory, we will study the source coding techniques with and without loss. Then we analyze the correcting codes for block errors, convutional and concatenated used in current systems.
Network coding, a relatively new area of research, has evolved from the theoretical level to become a tool used to optimize the performance of communication networks – wired, cellular, ad hoc, etc. The idea consists of mixing “packets” of data together when routing them from source to destination. Since network coding increases the network performance, it becomes a tool to enhance the existing protocols and algorithms in a network or for applications such as peer-to-peer and TCP. This book delivers an understanding of network coding and provides a set of studies showing the improvements in security, capacity and performance of fixed and mobile networks. This is increasingly topical as industry is increasingly becoming more reliant upon and applying network coding in multiple applications. Many cases where network coding is used in routing, physical layer, security, flooding, error correction, optimization and relaying are given – all of which are key areas of interest. Network Coding is the ideal resource for university students studying coding, and researchers and practitioners in sectors of all industries where digital communication and its application needs to be correctly understood and implemented. Contents 1. Network Coding: From Theory to Practice, Youghourta Benfattoum, Steven Martin and Khaldoun Al Agha. 2. Fountain Codes and Network Coding for WSNs, Anya Apavatjrut, Claire Goursaud, Katia Jaffrès-Runser and Jean-Marie Gorce. 3. Switched Code for Ad Hoc Networks: Optimizing the Diffusion by Using Network Coding, Nour Kadi and Khaldoun Al Agha. 4. Security by Network Coding, Katia Jaffrès-Runser and Cédric Lauradoux. 5. Security for Network Coding, Marine Minier, Yuanyuan Zhang and Wassim Znaïdi. 6. Random Network Coding and Matroids, Maximilien Gadouleau. 7. Joint Network-Channel Coding for the Semi-Orthogonal MARC: Theoretical Bounds and Practical Design, Atoosa Hatefi, Antoine O. Berthet and Raphael Visoz. 8. Robust Network Coding, Lana Iwaza, Marco Di Renzo and Michel Kieffer. 9. Flow Models and Optimization for Network Coding, Eric Gourdin and Jeremiah Edwards.
This contributed volume showcases the most significant results obtained from the DFG Priority Program on Compressed Sensing in Information Processing. Topics considered revolve around timely aspects of compressed sensing with a special focus on applications, including compressed sensing-like approaches to deep learning; bilinear compressed sensing - efficiency, structure, and robustness; structured compressive sensing via neural network learning; compressed sensing for massive MIMO; and security of future communication and compressive sensing.
Compressed sensing is a relatively recent area of research that refers to the recovery of high-dimensional but low-complexity objects from a limited number of measurements. The topic has applications to signal/image processing and computer algorithms, and it draws from a variety of mathematical techniques such as graph theory, probability theory, linear algebra, and optimization. The author presents significant concepts never before discussed as well as new advances in the theory, providing an in-depth initiation to the field of compressed sensing. An Introduction to Compressed Sensing contains substantial material on graph theory and the design of binary measurement matrices, which is missing in recent texts despite being poised to play a key role in the future of compressed sensing theory. It also covers several new developments in the field and is the only book to thoroughly study the problem of matrix recovery. The book supplies relevant results alongside their proofs in a compact and streamlined presentation that is easy to navigate. The core audience for this book is engineers, computer scientists, and statisticians who are interested in compressed sensing. Professionals working in image processing, speech processing, or seismic signal processing will also find the book of interest.
At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domains. In the areas of applied mathematics, electrical engineering, and theoretical computer science, an explosion of research activity has already followed the theoretical results that highlighted the efficiency of the basic principles. The elegant ideas behind these principles are also of independent interest to pure mathematicians. A Mathematical Introduction to Compressive Sensing gives a detailed account of the core theory upon which the field is build. With only moderate prerequisites, it is an excellent textbook for graduate courses in mathematics, engineering, and computer science. It also serves as a reliable resource for practitioners and researchers in these disciplines who want to acquire a careful understanding of the subject. A Mathematical Introduction to Compressive Sensing uses a mathematical perspective to present the core of the theory underlying compressive sensing.
Channel Coding in the Presence of Side Information reviews the concepts and methods of communication systems equipped with side information both from the theoretical and practical points of view. It is a comprehensive review that gives the reader an insightful introduction to one of the most important topics in modern communications systems.
The topic of the proposed book is signal compression. The compression (or low bit rate coding) of speech, audio, image and video signals is a key technology for rapidly emerging opportunities in multimedia products and services.The book contains chapters dedicated to the subtopics of data, speech, audio and visual signal coding, together with an introductory overview chapter on signal compression. The overview article summarizes current capabilities and future trends. The signal-specific chapters that follow focus on the latest technologies and coding standards, while including self-contained introductions to the respective signal domains. The authors of the book chapters are recognized experts in the field of signal processing, compression in particular.Signal compression dealing with both audio and visual signals technology has progressed very rapidly. The proposed book fills a clear void, and should prove to be a valuable reference, both to the practicing professional and to the relatively uninitiated student.
