Improved Modeling of the Correlation Between Continuous-Valued Sources in LDPC-Based DSC

TL;DR

This paper introduces a multi-BSC correlation model for continuous-valued sources in LDPC-based distributed source coding, improving accuracy and system performance without increasing complexity.

Contribution

It proposes a novel multi-BSC correlation model for continuous sources and integrates it into LDPC-based DSC systems, enhancing modeling accuracy.

Findings

The multi-BSC model better captures source correlation.

Simulation shows improved compression efficiency.

System complexity remains unchanged.

Abstract

Accurate modeling of the correlation between the sources plays a crucial role in the efficiency of distributed source coding (DSC) systems. This correlation is commonly modeled in the binary domain by using a single binary symmetric channel (BSC), both for binary and continuous-valued sources. We show that "one" BSC cannot accurately capture the correlation between continuous-valued sources; a more accurate model requires "multiple" BSCs, as many as the number of bits used to represent each sample. We incorporate this new model into the DSC system that uses low-density parity-check (LDPC) codes for compression. The standard Slepian-Wolf LDPC decoder requires a slight modification so that the parameters of all BSCs are integrated in the log-likelihood ratios (LLRs). Further, using an interleaver the data belonging to different bit-planes are shuffled to introduce randomness in the binary domain. The new system has the same complexity and delay as the standard one. Simulation results prove the effectiveness of the proposed model and system.