Distributed source coding : theory, algorithms, and applications
The advent of wireless sensor technology and ad-hoc networks has made DSC a major field of interest. Edited and written by the leading players in the field, this book presents the latest theory, algorithms and applications, making it the definitive reference on DSC for systems designers and implemen...
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| Hlavní autoři: | , |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Academic Press/Elsevier
2009
Elsevier Science & Technology Academic Press |
| Vydání: | 1 |
| Témata: | |
| ISBN: | 9780123744852, 0123744857 |
| On-line přístup: | Získat plný text |
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- Chapter 9. Model-based Multiview Video Compression Using Distributed Source Coding Principles -- 9.1 Introduction -- 9.2 Model Tracking -- 9.2.1 Image Appearance Model of a Rigid Object -- 9.2.2 Inverse Compositional Estimation of 3D Motion and Illumination -- 9.3 Distributed Compression Schemes -- 9.3.1 Feature Extraction and Coding -- 9.3.2 Types of Frames -- 9.3.3 Types of Side Information -- 9.4 Experimental Results -- 9.5 Conclusions -- References -- Chapter 10. Distributed Compression of Hyperspectral Imagery -- 10.1 Introduction -- 10.1.1 Hyperspectral Imagery Compression: State of the Art -- 10.1.2 Outline of This Chapter -- 10.2 Hyperspectral Image Compression -- 10.2.1 Dataset Characteristics -- 10.2.2 Intraband Redundancy and Cross-band Correlation -- 10.2.3 Limitations of Existing Hyperspectral Compression Techniques -- 10.3 DSC-based Hyperspectral Image Compression -- 10.3.1 Potential Advantages of DSC-based Hyperspectral Compression -- 10.3.2 Challenges in Applying DSC for Hyperspectral Imaging -- 10.4 Example Designs -- 10.4.1 DSC Techniques for Lossless Compression of HyperspectralImages -- 10.4.2 Wavelet-based Slepian-Wolf Coding for Lossy-to-lossless Compression of Hyperspectral Images -- 10.4.3 Distributed Compression of Multispectral Images Using a Set Theoretic Approach -- 10.5 Conclusions -- References -- Chapter 11. Securing Biometric Data -- 11.1 Introduction -- 11.1.1 Motivation and Objectives -- 11.1.2 Architectures and System Security -- 11.1.3 Chapter Organization -- 11.2 Related Work -- 11.3 Overview of Secure Biometrics Using Syndromes -- 11.3.1 Notation -- 11.3.2 Enrollment and Authentication -- 11.3.3 Performance Measures: Security and Robustness -- 11.3.4 Quantifying Security -- 11.3.5 Implementation Using Syndrome Coding -- 11.4 Iris System -- 11.4.1 Enrollment and Authentication -- 11.4.2 Experimental Results
- Front Cover -- Distributed Source Coding -- Copyright Page -- Table of Contents -- List of Contributors -- Introduction -- Part I: Theory -- Chapter 1. Foundations of Distributed Source Coding -- 1.1 Introduction -- 1.2 Centralized Source Coding -- 1.2.1 Lossless Source Coding -- 1.2.2 Lossy Source Coding -- 1.2.3 Lossy Source Coding for Sources with Memory -- 1.2.4 Some Notes on Practical Considerations -- 1.3 Distributed Source Coding -- 1.3.1 Lossless Source Coding -- 1.3.2 Lossy Source Coding -- 1.3.3 Interaction -- 1.4 Remote Source Coding -- 1.4.1 Centralized -- 1.4.2 Distributed: The CEO Problem -- 1.5 Joint Source-channel Coding -- Acknowledgments -- Appendix A: Formal Definitions and Notations -- A.1 Notation -- A.1.1 Centralized Source Coding -- A.1.2 Distributed Source Coding -- A.1.3 Remote Source Coding -- References -- Chapter 2. Distributed Transform Coding -- 2.1 Introduction -- 2.2 Foundations of Centralized Transform Coding -- 2.2.1 Transform Coding Overview -- 2.2.2 Lossless Compression -- 2.2.3 Quantizers -- 2.2.4 Bit Allocation -- 2.2.5 Transforms -- 2.2.6 Linear Approximation -- 2.3 The Distributed Karhunen--Loève Transform -- 2.3.1 Problem Statement and Notation -- 2.3.2 The Two-terminal Scenario -- 2.3.3 The Multiterminal Scenario and the Distributed KLT Algorithm -- 2.4 Alternative Transforms -- 2.4.1 Practical Distributed Transform Coding with Side Information -- 2.4.2 High-rate Analysis of Source Coding with Side Informationat Decoder -- 2.5 New Approaches to Distributed Compression with FRI -- 2.5.1 Background on Sampling of 2D FRI Signals -- 2.5.2 Detailed Example: Coding Scheme for Translatinga Bi-level Polygon -- 2.6 Conclusions -- References -- Chapter 3. Quantization for Distributed Source Coding -- 3.1 Introduction -- 3.2 Formulation of the Problem -- 3.2.1 Conventions -- 3.2.2 Network Distributed Source Coding
- 6.2.2 Practical Code Design Based on Channel Codes -- 6.2.3 Rate Adaptation -- 6.3 Nonasymmetric SW Coding -- 6.3.1 Time Sharing -- 6.3.2 The Parity Approach -- 6.3.3 The Syndrome Approach -- 6.3.4 Source Splitting -- 6.3.5 Rate Adaptation -- 6.4 Advanced Topics -- 6.4.1 Practical Code Design Based on Source Codes -- 6.4.2 Generalization to Nonbinary Sources -- 6.4.3 Generalization to M Sources -- 6.5 Conclusions -- References -- Chapter 7. Distributed Compression in Microphone Arrays -- 7.1 Introduction -- 7.2 Spatiotemporal Evolution of the Sound Field -- 7.2.1 Recording Setups -- 7.2.2 Spectral Characteristics -- 7.2.3 Spatiotemporal Sampling and Reconstruction -- 7.3 Huygens's Configuration -- 7.3.1 Setup -- 7.3.2 Coding Strategies -- 7.3.3 Rate-distortion Trade-offs -- 7.4 Binaural Hearing Aid Configuration -- 7.4.1 Setup -- 7.4.2 Coding Strategies -- 7.4.3 Rate-distortion Trade-offs -- 7.5 Conclusions -- Acknowledgment -- References -- Chapter 8. Distributed Video Coding: Basics, Codecs, and Performance -- 8.1 Introduction -- 8.2 Basics on Distributed Video Coding -- 8.3 The Early Wyner--Ziv Video Coding Architectures -- 8.3.1 The Stanford WZ Video Codec -- 8.3.2 The Berkeley WZ Video Codec -- 8.3.3 Comparing the Early WZ Video Codecs -- 8.4 Further Developments on Wyner-Ziv Video Coding -- 8.4.1 Improving RD Performance -- 8.4.2 Removing the Feedback Channel -- 8.4.3 Improving Error Resilience -- 8.4.4 Providing Scalability -- 8.5 The DISCOVER Wyner-Ziv Video Codec -- 8.5.1 Transform and Quantization -- 8.5.2 Slepian-Wolf Coding -- 8.5.3 Side Information Creation -- 8.5.4 Correlation Noise Modeling -- 8.5.5 Reconstruction -- 8.6 The DISCOVER Codec Performance -- 8.6.1 Performance Evaluation Conditions -- 8.6.2 RD Performance Evaluation -- 8.6.3 Complexity Performance Evaluation -- 8.7 Final Remarks -- Acknowledgments -- References
- 11.5 Fingerprint System: Modeling Approach -- 11.5.1 Minutiae Representation of Fingerprints -- 11.5.2 Modeling the Movement of Fingerprint Minutiae -- 11.5.3 Experimental Evaluation of Security and Robustness -- 11.5.4 Remarks on the Modeling Approach -- 11.6 Fingerprint System: Transformation Approach -- 11.6.1 Desired Statistical Properties of Feature Vectors -- 11.6.2 Feature Transformation Algorithm -- 11.6.3 Experimental Evaluation of Security and Robustness -- 11.7 Summary -- References -- Index
- 3.2.3 Cost, Distortion, and Rate Measures -- 3.2.4 Optimal Quantizers and Reconstruction Functions -- 3.2.5 Example: Quantization of Side Information -- 3.3 Optimal Quantizer Design -- 3.3.1 Optimality Conditions -- 3.3.2 Lloyd Algorithm for Distributed Quantization -- 3.4 Experimental Results -- 3.5 High-rate Distributed Quantization -- 3.5.1 High-rate WZ Quantization of Clean Sources -- 3.5.2 High-rate WZ Quantization of Noisy Sources -- 3.5.3 High-rate Network Distributed Quantization -- 3.6 Experimental Results Revisited -- 3.7 Conclusions -- References -- Chapter 4. Zero-error Distributed Source Coding -- 4.1 Introduction -- 4.2 Graph Theoretic Connections -- 4.2.1 VLZE Coding and Graphs -- 4.2.2 Basic Definitions and Notation -- 4.2.3 Graph Entropies -- 4.2.4 Graph Capacity -- 4.3 Complementary Graph Entropy and VLZE Coding -- 4.4 Network Extensions -- 4.4.1 Extension 1: VLZE Coding When Side Information May Be Absent -- 4.4.2 Extension 2: VLZE Coding with Compound Side Information -- 4.5 VLZE Code Design -- 4.5.1 Hardness of Optimal Code Design -- 4.5.2 Hardness of Coding with Length Constraints -- 4.5.3 An Exponential-time Optimal VLZE Code Design Algorithm -- 4.6 Conclusions -- References -- Chapter 5. Distributed Coding of Sparse Signals -- 5.1 Introduction -- 5.1.1 Sparse Signals -- 5.1.2 Signal Recovery with Compressive Sampling -- 5.2 Compressive Sampling as Distributed Source Coding -- 5.2.1 Modeling Assumptions -- 5.2.2 Analyses -- 5.2.3 Numerical Simulation -- 5.3 Information Theory to the Rescue? -- 5.4 Conclusions-Whither Compressive Sampling? -- Appendix -- 5.5 Quantizer Performance and Quantization Error -- Acknowledgments -- References -- Part II: Algorithms and Applications -- Chapter 6. Toward Constructive Slepian-Wolf Coding Schemes -- 6.1 Introduction -- 6.2 Asymmetric SW Coding -- 6.2.1 Principle of Asymmetric SW Coding
- Intro -- Table of Contents -- List of Contributors -- Introduction -- Part I: Theory -- Chapter 1. Foundations of Distributed Source Coding -- 1.1 Introduction -- 1.2 Centralized Source Coding -- 1.2.1 Lossless Source Coding -- 1.2.2 Lossy Source Coding -- 1.2.3 Lossy Source Coding for Sources with Memory -- 1.2.4 Some Notes on Practical Considerations -- 1.3 Distributed Source Coding -- 1.3.1 Lossless Source Coding -- 1.3.2 Lossy Source Coding -- 1.3.3 Interaction -- 1.4 Remote Source Coding -- 1.4.1 Centralized -- 1.4.2 Distributed: The CEO Problem -- 1.5 Joint Source-channel Coding -- Acknowledgments -- Appendix A: Formal Definitions and Notations -- A.1 Notation -- A.1.1 Centralized Source Coding -- A.1.2 Distributed Source Coding -- A.1.3 Remote Source Coding -- References -- Chapter 2. Distributed Transform Coding -- 2.1 Introduction -- 2.2 Foundations of Centralized Transform Coding -- 2.2.1 Transform Coding Overview -- 2.2.2 Lossless Compression -- 2.2.3 Quantizers -- 2.2.4 Bit Allocation -- 2.2.5 Transforms -- 2.2.6 Linear Approximation -- 2.3 The Distributed Karhunen--Loève Transform -- 2.3.1 Problem Statement and Notation -- 2.3.2 The Two-terminal Scenario -- 2.3.3 The Multiterminal Scenario and the Distributed KLT Algorithm -- 2.4 Alternative Transforms -- 2.4.1 Practical Distributed Transform Coding with Side Information -- 2.4.2 High-rate Analysis of Source Coding with Side Informationat Decoder -- 2.5 New Approaches to Distributed Compression with FRI -- 2.5.1 Background on Sampling of 2D FRI Signals -- 2.5.2 Detailed Example: Coding Scheme for Translatinga Bi-level Polygon -- 2.6 Conclusions -- References -- Chapter 3. Quantization for Distributed Source Coding -- 3.1 Introduction -- 3.2 Formulation of the Problem -- 3.2.1 Conventions -- 3.2.2 Network Distributed Source Coding -- 3.2.3 Cost, Distortion, and Rate Measures
- 11.5 Fingerprint System: Modeling Approach -- 11.5.1 Minutiae Representation of Fingerprints -- 11.5.2 Modeling the Movement of Fingerprint Minutiae -- 11.5.3 Experimental Evaluation of Security and Robustness -- 11.5.4 Remarks on the Modeling Approach -- 11.6 Fingerprint System: Transformation Approach -- 11.6.1 Desired Statistical Properties of Feature Vectors -- 11.6.2 Feature Transformation Algorithm -- 11.6.3 Experimental Evaluation of Security and Robustness -- 11.7 Summary -- References -- Index -- Index
- 3.2.4 Optimal Quantizers and Reconstruction Functions -- 3.2.5 Example: Quantization of Side Information -- 3.3 Optimal Quantizer Design -- 3.3.1 Optimality Conditions -- 3.3.2 Lloyd Algorithm for Distributed Quantization -- 3.4 Experimental Results -- 3.5 High-rate Distributed Quantization -- 3.5.1 High-rate WZ Quantization of Clean Sources -- 3.5.2 High-rate WZ Quantization of Noisy Sources -- 3.5.3 High-rate Network Distributed Quantization -- 3.6 Experimental Results Revisited -- 3.7 Conclusions -- References -- Chapter 4. Zero-error Distributed Source Coding -- 4.1 Introduction -- 4.2 Graph Theoretic Connections -- 4.2.1 VLZE Coding and Graphs -- 4.2.2 Basic Definitions and Notation -- 4.2.3 Graph Entropies -- 4.2.4 Graph Capacity -- 4.3 Complementary Graph Entropy and VLZE Coding -- 4.4 Network Extensions -- 4.4.1 Extension 1: VLZE Coding When Side Information May Be Absent -- 4.4.2 Extension 2: VLZE Coding with Compound Side Information -- 4.5 VLZE Code Design -- 4.5.1 Hardness of Optimal Code Design -- 4.5.2 Hardness of Coding with Length Constraints -- 4.5.3 An Exponential-time Optimal VLZE Code Design Algorithm -- 4.6 Conclusions -- References -- Chapter 5. Distributed Coding of Sparse Signals -- 5.1 Introduction -- 5.1.1 Sparse Signals -- 5.1.2 Signal Recovery with Compressive Sampling -- 5.2 Compressive Sampling as Distributed Source Coding -- 5.2.1 Modeling Assumptions -- 5.2.2 Analyses -- 5.2.3 Numerical Simulation -- 5.3 Information Theory to the Rescue? -- 5.4 Conclusions-Whither Compressive Sampling? -- Appendix -- 5.5 Quantizer Performance and Quantization Error -- Acknowledgments -- References -- Part II: Algorithms and Applications -- Chapter 6. Toward Constructive Slepian-Wolf Coding Schemes -- 6.1 Introduction -- 6.2 Asymmetric SW Coding -- 6.2.1 Principle of Asymmetric SW Coding