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.

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 implementers, researchers, and graduate students. This book gives a clear understanding of the performance limits of distributed source coders for specific classes of sources and presents the design and application of practical algorithms for realistic scenarios. Material covered includes the use of standard channel codes, such as LDPC and Turbo codes, to DSC, and discussion of the suitability of compressed sensing for distributed compression of sparse signals. Extensive applications are presented and include distributed video coding, microphone arrays and securing biometric data. Clear explanation of the principles of distributed source coding (DSC), a technology that has applications in sensor networks, ad-hoc networks, and distributed wireless video systems for surveillance Edited and written by the leading players in the field, providing a complete and authoritative reference Contains all the latest theory, practical algorithms for DSC design and the most recently developed applications

Conventional multimedia compression leverages the source statistics at the encoder side. This is not suitable for some emerging applications such as wireless sensor networks, where the encoders usually have limited functionalities and power supplies, therefore it is desired to shift the bulk of computational burden to the decoder side. The resulting new coding paradigm is called distributed source coding (DSC). Most practical DSC schemes only achieve good results when a priori knowledge about the source statistics is assumed. For DSC of real-world sources such as images and videos, such knowledge is not really available. In this dissertation, we focus on designing decoder-side learning schemes for better understanding of the source statistics, based on which practical DSC systems can be built for high-efficiency, low-cost, and secure multimedia communications. We have studied distributed video coding and compression of encrypted images and videos. We propose to enable partial access to the current source through progressive decoding, such that the decoder's knowledge about the source statistics can be progressively refined. The resulting schemes have achieved significant improvement in coding efficiency. We also studied the rate allocation problem to optimize the power consumption in transmitting multiple correlated sources over a wireless sensor network. The framework developed in this dissertation will provide significant insights and become important building blocks in distributed video applications, including those that are of significant importance to the national security, agriculture, economy, and healthcare.

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.

"In many sensor network applications, the sensor readings are inter-node correlated. In such cases, efficient gathering of sensor readings requires distributed compression. Distributed source coding provides practical solutions for compression of these correlated readings when the appropriate rates for the marginal encoding is known at the sensor nodes. In this thesis, we present a data-gathering technique for sensor networks that exploits correlation between sensor data at different locations in the network. Contrary to distributed source coding, our method does not rely on knowledge of the source correlation model in each node although this knowledge is required at the decoder node. Similar to network coding, our proposed method (which we call Quantized Network Coding) propagates mixtures of packets through the network. The main conceptual difference between our technique and other existing methods is that Quantized Network Coding operates on the field of real numbers and not on a finite field. In this thesis, we study our quantized network coding in both lossless and lossy networks.In the study of lossless networks, we discuss the theoretical foundations for our data gathering technique. By exploiting principles borrowed from compressed sensing, we show that the proposed technique can achieve a good approximation of the sensor readings at the sink node with only a few packets received, and that this approximation gets progressively better as the number of received packets increases. Our first approach is to explain the theoretical foundations for sparse recovery from quantized network coded packets based on an analysis of the Restricted Isometry Property of the corresponding measurement matrices. Extensive simulations comparing the proposed Quantized Network Coding to classic network coding and packet forwarding scenarios demonstrate the delay/distortion advantage of quantized network coding. Furthermore, we discuss the advantages of quantized network coding in a Bayesian scenario where the prior of the sensor readings is available at the decoder node. For such Bayesian scenarios, we also discuss the adaptation of a message passing based decoding algorithm with the aid of simulations.To study the practicality of quantized network coding in lossy networks, we adapt it into the IEEE 802.15.4 standard which characterizes low rate wireless communication for sensor networks. This is done by developing a comprehensive implementation of the PHY and MAC layers of the standard and then adjusting the MAC layer settings to match with our requirements. Our computer simulations using the developed implementation show a significant decrease of the delay in many simulation scenarios. The results obtained using this implementation show more advantages for quantized network coding compared to classic routing based protocols especially for high packet drop rates." --