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.
Autorentext
SHUANG WANG, University of California, San Diego, USA
YONG FANG, Northwest A&F University, China
SAMUEL CHENG, University of Oklahoma, USA
Klappentext
Understanding distributed source coding from theory to practice
Distributed source coding is one of the key enablers for ef cient 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 I covers the background of information theory with an emphasis on distributed source coding, Part II discusses designs of algorithmic solutions for distributed source coding problems, covering the three most important distributed source coding problems (Slepian–Wolf, Wyner–Ziv, and MT source coding), and Part III is dedicated to a variety of potential distributed source coding 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 rst-year graduate students in electrical engineering, computer engineering, signal processing, image/video processing, and information theory and communications.
Inhalt
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 64
Collaborative Lower Bound 66
-sum Bound 67
Part II Implementation 75
5 Slepian-Wolf Code Designs Based on Channel Coding 77
5.1 Asymmetric SWCoding 77
5.1.1 Binning Idea 78
5.1.2 Syndrome-based Approach 79
Hamming Binning 80
SWEncoding 80
SWDecoding 80
LDPC-based SWCoding 81
5.1.3 Parity-based Approach 82
5.1.4 Syndrome-based Versus Parity-based Approach 84
5.2 Non-asymmetric SWCoding 85
5.2.1 Generalized Syndrome-based Approach 86
5.2.2 Implementation using IRA Codes 88
5.3 Adaptive Slepian-Wolf Coding 90
5.3.1 Particle-based Belief Propagation for SWCoding 91
5.4 Latest Developments and Trends 93
6 Distributed Arithmetic Coding 97
6.1 Arithmetic Coding 97
6.2 Distributed Arithmetic Coding 101
6.3 Definition of the DAC Spectrum 103
6.3.1 Motivations 103
6.3.2 Initial DAC Spectrum 104
6.3.3 Depth-i DAC Spectrum 105
6.3.4 Some Simple Properties of the DAC Spectrum 107
6.4 Formulation of the Initial DAC Spectrum 107
6.5 Explicit Form of the Initial DAC Spectrum 110
6.6 Evolution of the DAC Spectrum 113
6.7 Numerical Calculation of the DAC Spectrum 116
6.7.1 Numerical Calculation of the Initial DAC Spectrum 117
6.7.2 Numerical Estimation of DAC Spectrum Evolution 118
6.8 Analyses on DAC Codes with Spectrum 120
6.8.1 Definition of DAC Codes 121
6.8.2 Codebook Cardinality 122
6.8.3 Codebook Index Distribution 123
6.8.4 Rate Loss 123
6.8.5 Decoder Complexity 124
6.8.6 Decoding Error Probability 126
6.9 Improved Binary DAC Codec 130
6.9.1 Permutated BDAC Codec 130
Principle 130
Proof of SWLimit Achievability 131
6.9.2 BDAC Decoder withWeighted Branching 132
6.10 Implementation of the Improved BDAC Codec 134
6.10.1 Encoder 134
Principle 134
Implementation 135
6.10.2 Decoder 135
Principle 135
Implementation 136
6.11 Experimental Results 138
Effect of Segment Size on Permutation Technique 139
Effect of Surviving-Path Number onWB Technique 139
Comparison with LDPC Codes 139
Application of PBDAC to Nonuniform Sources 140
6.12 Conclusion 141
7 Wyner-Ziv Code Design 143
7.1 Vector Quantization 143
7.2 Lattice Theory 146
7.2.1 What is a Lattice? 146
Examples 146
Dual Lattice 147
Integral Lattice 147
Lattice Quantization 148
7.…