LDPC-Based Iterative Algorithm for Compression of Correlated Sources at Rates Approaching the Slepian-Wolf Bound

TL;DR

This paper introduces a new LDPC-based iterative algorithm for compressing correlated sources efficiently, approaching the theoretical Slepian-Wolf limit by leveraging estimated source correlation at the decoder.

Contribution

It presents a novel LDPC-based iterative compression algorithm that adapts to estimated source correlation, improving compression rates near the Slepian-Wolf bound.

Findings

Significant compression gains achieved with the proposed method.

Performance approaches the Slepian-Wolf theoretical limit.

Algorithm effectively estimates and exploits source correlation.

Abstract

This article proposes a novel iterative algorithm based on Low Density Parity Check (LDPC) codes for compression of correlated sources at rates approaching the Slepian-Wolf bound. The setup considered in the article looks at the problem of compressing one source at a rate determined based on the knowledge of the mean source correlation at the encoder, and employing the other correlated source as side information at the decoder which decompresses the first source based on the estimates of the actual correlation. We demonstrate that depending on the extent of the actual source correlation estimated through an iterative paradigm, significant compression can be obtained relative to the case the decoder does not use the implicit knowledge of the existence of correlation.