Distributed Source Coding Theory and Practice

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.

SHUANG WANG, University of California, San Diego, USA

YONG FANG, Northwest A&F University, China

SAMUEL CHENG, University of Oklahoma, USA

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

𝜇-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

7.2.2 What is a Good Lattice? 149

Packing Efficiency 149

Covering Efficiency 150

Normalized Second Moment 150

Kissing Number 150

Some Good Lattices 151

7.3 Nested Lattice Quantization 151

Encoding/decoding 152

Coset Binning 152

Quantization Loss and Binning Loss 153

SW Coded NLQ 154

7.3.1 Trellis Coded Quantization 154

7.3.2 Principle of TCQ 155

Generation of Codebooks 156

Generation of Trellis from Convolutional Codes 156

Mapping of Trellis Branches onto Sub-codebooks 157

Quantization 157

Example 158

7.4 WZ Coding Based on TCQ and LDPC Codes 159

7.4.1 Statistics of TCQ Indices 159

7.4.2 LLR of Trellis Bits 162

7.4.3 LLR of Codeword Bits 163

7.4.4 Minimum MSE Estimation 163

7.4.5 Rate Allocation of Bit-planes 164

7.4.6 Experimental Results 166

Part III Applications 167

8 Wyner–Ziv Video Coding 169

8.1 Basic Principle 169

8.2 Benefits of WZ Video Coding 170

8.3 Key Components of WZ Video Decoding 171

8.3.1 Side-information Preparation 171

Bidirectional Motion Compensation 172

8.3.2 Correlation Modeling 173

Exploiting Spatial Redundancy 174

8.3.3 Rate Controller 175

8.4 Other Notable Features of Miscellaneous WZ Video Coders 175

9 Correlation Estimation in DVC 177

9.1 Background to Correlation Parameter Estimation in DVC 177

9.1.1 Correlation Model inWZ Video Coding 177

9.1.2 Offline Correlation Estimation 178

Pixel Domain Offline Correlation Estimation 178

Transform Domain Offline Correlation Estimation 180

9.1.3 Online Correlation Estimation 181

Pixel Domain Online Correlation Estimation 182

Transform Domain Online Correlation Estimation 184

9.2 Recap of Belief Propagation and Particle Filter Algorithms 185

9.2.1 Belief Propagation Algorithm 185

9.2.2 Particle Filtering 186

9.3 Correlation Estimation in DVC with Particle Filtering 187

9.3.1 Factor Graph Construction 187

9.3.2 Correlation Estimation in DVC with Particle Filtering 190

9.3.3 Experimental Results 192

9.3.4 Conclusion 197

9.4 Low Complexity Correlation Estimation using Expectation Propagation 199

9.4.1 System Architecture 199

9.4.2 Factor Graph Construction 199

Joint Bit-plane SWCoding (Region II) 200

Correlation Parameter Tracking (Region I) 201

9.4.3 Message Passing on the Constructed Factor Graph 202

Expectation Propagation 203

9.4.4 Posterior Approximation of the Correlation Parameter using Expectation Propagation 204

Moment Matching 205

9.4.5 Experimental Results 206

9.4.6 Conclusion 211

10 DSC for Solar Image Compression 213

10.1 Background 213

10.2 RelatedWork 215

10.3 Distributed Multi-view Image Coding 217

10.4 Adaptive Joint Bit-plane WZ Decoding of Multi-view Images with Disparity Estimation 217

10.4.1 Joint Bit-planeWZ Decoding 217

10.4.2 Joint Bit-planeWZ Decoding with Disparity Estimation 219

10.4.3 Joint Bit-planeWZ Decoding with Correlation Estimation 220

10.5 Results and Discussion 221

10.6 Summary 224

11 Secure Distributed Image Coding 225

11.1 Background 225

11.2 System Architecture 227

11.2.1 Compression of Encrypted Data 228

11.2.2 Joint Decompression and Decryption Design 230

11.3 Practical Implementation Issues 233

11.4 Experimental Results 233

11.4.1 Experiment Setup 234

11.4.2 Security and Privacy Protection 235

11.4.3 Compression Performance 236

11.5 Discussion 239

12 Secure Biometric Authentication Using DSC 241

12.1 Background 241

12.2 RelatedWork 243

12.3 System Architecture 245

12.3.1 Feature Extraction 246

12.3.2 Feature Pre-encryption 248

12.3.3 SeDSC Encrypter/decrypter 248

12.3.4 Privacy-preserving Authentication 249

12.4 SeDSC Encrypter Design 249

12.4.1 Non-asymmetric SWCodes with Code Partitioning 250

12.4.2 Implementation of SeDSC Encrypter using IRA Codes 251

12.5 SeDSC Decrypter Design 252

12.6 Experiments 256

12.6.1 Dataset and Experimental Setup 256

12.6.2 Feature Length Selection 257

12.6.3 Authentication Accuracy 257

Authentication Performances on Small Feature Length (i.e., N = 100) 257

Performances on Large Feature Lengths (i.e., N ≥ 300) 258

12.6.4 Privacy and Security 259

12.6.5 Complexity Analysis 261

12.7 Discussion 261

A Basic Information Theory 263

A.1 Information Measures 263

A.1.1 Entropy 263

A.1.2 Relative Entropy 267

A.1.3 Mutual Information 268

A.1.4 Entropy Rate 269

A.2 Independence and Mutual Information 270

A.3 Venn Diagram Interpretation 273

A.4 Convexity and Jensen’s Inequality 274

A.5 Differential Entropy 277

A.5.1 Gaussian Random Variables 278

A.5.2 Entropy Power Inequality 278

A.6 Typicality 279

A.6.1 Jointly Typical Sequences 282

A.7 Packing Lemmas and Covering Lemmas 284

A.8 Shannon’s Source CodingTheorem 286

A.9 Lossy Source Coding—Rate-distortionTheorem 289

A.9.1 Rate-distortion Problem with Side Information 291

B Background on Channel Coding 293

B.1 Linear Block Codes 294

B.1.1 Syndrome Decoding of Block Codes 295

B.1.2 Hamming Codes, Packing Bound, and Perfect Codes 295

B.2 Convolutional Codes 297

B.2.1 Viterbi Decoding Algorithm 298

B.3 Shannon’s Channel CodingTheorem 301

B.3.1 Achievability Proof of the Channel CodingTheorem 303

B.3.2 Converse Proof of Channel CodingTheorem 305

B.4 Low-density Parity-check Codes 306

B.4.1 A Quick Summary of LDPC Codes 306

B.4.2 Belief Propagation Algorithm 307

B.4.3 LDPC Decoding using BP 312

B.4.4 IRA Codes 314

C Approximate Inference 319

C.1 Stochastic Approximation 319

C.1.1 Importance SamplingMethods 320

C.1.2 Markov Chain Monte Carlo 321

Markov Chains 321

Markov Chain Monte Carlo 321

C.2 Deterministic Approximation 322

C.2.1 Preliminaries 322

Exponential Family 322

Kullback–Leibler Divergence 323

Assumed-density Filtering 324

C.2.2 Expectation Propagation 325

Relationship with BP 326

C.2.3 Relationship with Other Variational Inference Methods 328

D Multivariate Gaussian Distribution 331

D.1 Introduction 331

D.2 Probability Density Function 331

D.3 Marginalization 332

D.4 Conditioning 333

D.5 Product of Gaussian pdfs 334

D.6 Division of Gaussian pdfs 337

D.7 Mixture of Gaussians 337

D.7.1 Reduce the Number of Components in Gaussian Mixtures 338

Which Components to Merge? 340

How to Merge Components? 341

D.8 Summary 342

Appendix: Matrix Equations 343

Bibliography 345

Index 357

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