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Compression via channel coding - Distributed source coding for sensor networks

Zixiang Xiong, Angelos D. Liveris, Soofin Cheng

Open publisher page 751 citations

Abstract

In recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. It relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies in sensor networks is the distributed source coding (DSC), which refers to the compression of the multiple correlated sensor outputs that does not communicate with each other. DSC allows a many-to-one video coding paradigm that effectively swaps encoder-decoder complexity with respect to conventional video coding, thereby representing a fundamental concept shift in video processing. This article has presented an intensive discussion on two DSC techniques, namely Slepian-Wolf coding and Wyner-Ziv coding. The Slepian and Wolf coding have theoretically shown that separate encoding is as efficient as joint coding for lossless compression in channel coding.

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What this paper is about

In recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. It relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies in sensor networks is the distributed source coding (DSC), which refers to the compression of the multiple correlated sensor outputs that does not communicate with each other. DSC allows a many-to-one video coding paradigm that effectively swaps encoder-decoder complexity with respect to conventional video coding, thereby representing a fundamental concept shift in video processing. This article has presented an intensive discussion on two DSC techniques, namely Slepian-Wolf coding and Wyner-Ziv coding. The Slepian and Wolf coding have theoretically shown that separate encoding is as efficient as joint coding for lossless compression in channel coding.

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Available abstract

In recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. It relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies in sensor networks is the distributed source coding (DSC), which refers to the compression of the multiple correlated sensor outputs that does not communicate with each other. DSC allows a many-to-one video coding paradigm that effectively swaps encoder-decoder complexity with respect to conventional video coding, thereby representing a fundamental concept shift in video processing. This article has presented an intensive discussion on two DSC techniques, namely Slepian-Wolf coding and Wyner-Ziv coding. The Slepian and Wolf coding have theoretically shown that separate encoding is as efficient as joint coding for lossless compression in channel coding.

Key concepts: Tunstall coding, Distributed source coding, Context-adaptive binary arithmetic coding, Computer science, Encoder, Context-adaptive variable-length coding, Shannon–Fano coding, Coding (social sciences)

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