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Informationen zum Autor SHUANG WANG, University of California, San Diego, USA YONG FANG, Northwest A&F University, China SAMUEL CHENG, University of Oklahoma, USA Klappentext Distributed source coding is one of the key enablers for efficient cooperative communication. The potential applications range from wireless sensor networks, ad-hoc networks, and surveillance networks, to robust low-complexity video coding, stereo/Multiview video coding, HDTV, hyper-spectral and multispectral imaging, and biometrics.The book is divided into three sections: theory, algorithms, and applications. Part one covers the background of information theory with an emphasis on DSC; part two discusses designs of algorithmic solutions for DSC problems, covering the three most important DSC problems: Slepian-Wolf, Wyner-Ziv, and MT source coding; and part three is dedicated to a variety of potential DSC applications.Key features:* Clear explanation of distributed source coding theory and algorithms including both lossless and lossy designs.* Rich applications of distributed source coding, which covers multimedia communication and data security applications.* Self-contained content for beginners from basic information theory to practical code implementation.The book provides fundamental knowledge for engineers and computer scientists to access the topic of distributed source coding. It is also suitable for senior undergraduate and first year graduate students in electrical engineering; computer engineering; signal processing; image/video processing; and information theory and communications. Zusammenfassung Distributed source coding is one of the key enablers for efficient cooperative communication. The potential applications range from wireless sensor networks, ad-hoc networks, and surveillance networks, to robust low-complexity video coding, stereo/Multiview video coding, HDTV, hyper-spectral and multispectral imaging, and biometrics. Inhaltsverzeichnis Preface xiii Acknowledgment xv About the Companion Website xvii 1 Introduction 1 1.1 What is Distributed Source Coding? 2 1.2 Historical Overview and Background 2 1.3 Potential and Applications 3 1.4 Outline 4 Part I Theory of Distributed Source Coding 7 2 Lossless Compression of Correlated Sources 9 2.1 Slepian-Wolf Coding 10 2.1.1 Proof of the SWTheorem 15 Achievability of the SWTheorem 16 Converse of the SWTheorem 19 2.2 Asymmetric and Symmetric SWCoding 21 2.3 SWCoding of Multiple Sources 22 3 Wyner-Ziv Coding Theory 25 3.1 Forward Proof ofWZ Coding 27 3.2 Converse Proof of WZ Coding 29 3.3 Examples 30 3.3.1 Doubly Symmetric Binary Source 30 Problem Setup 30 A Proposed Scheme 31 Verify the Optimality of the Proposed Scheme 32 3.3.2 Quadratic Gaussian Source 35 Problem Setup 35 Proposed Scheme 36 Verify the Optimality of the Proposed Scheme 37 3.4 Rate Loss of theWZ Problem 38 Binary Source Case 39 Rate loss of General Cases 39 4 Lossy Distributed Source Coding 41 4.1 Berger-Tung Inner Bound 42 4.1.1 Berger-Tung Scheme 42 Codebook Preparation 42 Encoding 42 Decoding 43 4.1.2 Distortion Analysis 43 4.2 Indirect Multiterminal Source Coding 45 4.2.1 Quadratic Gaussian CEO Problem with Two Encoders 45 Forward Proof of Quadratic Gaussian CEO Problem with Two Terminals 46 Converse Proof of Quadratic Gaussian CEO Problem with Two Terminals 48 4.3 Direct Multiterminal Source Coding 54 4.3.1 Forward Proof of Gaussian Multiterminal Source Coding Problem with Two Sources 55 4.3.2 Converse Proof of Gaussian Multiterminal Source Coding Problem with Two Sources 63 Bounds for R1 and R2 6...
