Compression via channel coding - Distributed source coding for sensor networks

TLDR

Sensor research is undergoing a quiet revolution with potential societal impact, relying on efficient, unattended, trustworthy systems, and distributed source coding enables compression of correlated sensor outputs without inter‑sensor communication. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. DSC allows a many‑to‑one video coding paradigm that swaps encoder‑decoder complexity relative to conventional video coding, representing a fundamental shift in video processing. The article discusses Slepian‑Wolf and Wyner‑Ziv coding, showing that Slepian‑Wolf coding achieves lossless compression efficiency equal to joint coding in channel coding.

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.