Distributed Source Coding Theory and Practice 1st Edition by Shuang Wang, Yong Fang, Samuel Cheng – Ebook PDF Instant Download/Delivery: 111870598X, 9781118705988
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Product details:
ISBN 10: 111870598X
ISBN 13: 9781118705988
Author: Shuang Wang, Yong Fang, Samuel Cheng
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.
Distributed Source Coding Theory and Practice 1st Table of contents:
Chapter 1: Introduction
1.1 What is Distributed Source Coding?
1.2 Historical Overview and Background
1.3 Potential and Applications
1.4 Outline
Part I: Theory of Distributed Source Coding
Chapter 2: Lossless Compression of Correlated Sources
2.1 Slepian–Wolf Coding
2.2 Asymmetric and Symmetric SW Coding
2.3 SW Coding of Multiple Sources
Chapter 3: Wyner–Ziv Coding Theory
3.1 Forward Proof of WZ Coding
3.2 Converse Proof of WZ Coding
3.3 Examples
3.4 Rate Loss of the WZ Problem
Chapter 4: Lossy Distributed Source Coding
4.1 Berger–Tung Inner Bound
4.2 Indirect Multiterminal Source Coding
4.3 Direct Multiterminal Source Coding
Part II: Implementation
Chapter 5: Slepian–Wolf Code Designs Based on Channel Coding
5.1 Asymmetric SW Coding
5.2 Non-asymmetric SW Coding
5.3 Adaptive Slepian–Wolf Coding
5.4 Latest Developments and Trends
Chapter 6: Distributed Arithmetic Coding
6.1 Arithmetic Coding
6.2 Distributed Arithmetic Coding
6.3 Definition of the DAC Spectrum
6.4 Formulation of the Initial DAC Spectrum
6.5 Explicit Form of the Initial DAC Spectrum
6.6 Evolution of the DAC Spectrum
6.7 Numerical Calculation of the DAC Spectrum
6.8 Analyses on DAC Codes with Spectrum
6.9 Improved Binary DAC Codec
6.10 Implementation of the Improved BDAC Codec
6.11 Experimental Results
6.12 Conclusion
Chapter 7: Wyner–Ziv Code Design
7.1 Vector Quantization
7.2 Lattice Theory
7.3 Nested Lattice Quantization
7.4 WZ Coding Based on TCQ and LDPC Codes
Part III: Applications
Chapter 8: Wyner–Ziv Video Coding
8.1 Basic Principle
8.2 Benefits of WZ Video Coding
8.3 Key Components of WZ Video Decoding
8.4 Other Notable Features of Miscellaneous WZ Video Coders
Chapter 9: Correlation Estimation in DVC
9.1 Background to Correlation Parameter Estimation in DVC
9.2 Recap of Belief Propagation and Particle Filter Algorithms
9.3 Correlation Estimation in DVC with Particle Filtering
9.4 Low Complexity Correlation Estimation using Expectation Propagation
Chapter 10: DSC for Solar Image Compression
10.1 Background
10.2 Related Work
10.3 Distributed Multi-view Image Coding
10.4 Adaptive Joint Bit-plane WZ Decoding of Multi-view Images with Disparity Estimation
10.5 Results and Discussion
10.6 Summary
Chapter 11: Secure Distributed Image Coding
11.1 Background
11.2 System Architecture
11.3 Practical Implementation Issues
11.4 Experimental Results
11.5 Discussion
Chapter 12: Secure Biometric Authentication Using DSC
12.1 Background
12.2 Related Work
12.3 System Architecture
12.4 SeDSC Encrypter Design
12.5 SeDSC Decrypter Design
12.6 Experiments
12.7 Discussion
Appendix A: Basic Information Theory
A.1 Information Measures
A.2 Independence and Mutual Information
A.3 Venn Diagram Interpretation
A.4 Convexity and Jensen’s Inequality
A.5 Differential Entropy
A.6 Typicality
A.7 Packing Lemmas and Covering Lemmas
A.8 Shannon’s Source Coding Theorem
A.9 Lossy Source Coding—Rate-distortion Theorem
Appendix B: Background on Channel Coding
B.1 Linear Block Codes
B.2 Convolutional Codes
B.3 Shannon’s Channel Coding Theorem
B.4 Low-density Parity-check Codes
Appendix C: Approximate Inference
C.1 Stochastic Approximation
C.2 Deterministic Approximation
Appendix D: Multivariate Gaussian Distribution
D.1 Introduction
D.2 Probability Density Function
D.3 Marginalization
D.4 Conditioning
D.5 Product of Gaussian pdfs
D.6 Division of Gaussian pdfs
D.7 Mixture of Gaussians
D.8 Summary
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