Lossy Source Compression of Non-Uniform Binary Sources Using GQ-LDGM Codes

TL;DR

This paper explores the use of GF(q)-quantized LDGM codes for lossy compression of non-uniform binary sources, achieving near-optimal performance through message-passing algorithms.

Contribution

It introduces a novel approach combining GF(q)-quantization with LDGM codes for efficient non-uniform binary source compression.

Findings

Performance close to theoretical rate-distortion bounds

Effective use of message-passing and decimation algorithms

Suitable for direct quantization of non-uniform Bernoulli sources

Abstract

In this paper, we study the use of GF(q)-quantized LDGM codes for binary source coding. By employing quantization, it is possible to obtain binary codewords with a non-uniform distribution. The obtained statistics is hence suitable for optimal, direct quantization of non-uniform Bernoulli sources. We employ a message-passing algorithm combined with a decimation procedure in order to perform compression. The experimental results based on GF(q)-LDGM codes with regular degree distributions yield performances quite close to the theoretical rate-distortion bounds.