Distributed source coding : theory and practice

Content: 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     7.2.2 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