Distributed Source Coding Using Continuous-Valued Syndromes

TL;DR

This paper introduces a continuous-valued syndrome approach for distributed source coding, achieving near-theoretical performance bounds with practical coding schemes for correlated Gaussian sources.

Contribution

It extends syndrome-based coding from discrete to continuous sources and demonstrates near-optimal performance with practical implementations.

Findings

Achieves within 3-4 dB of the Wyner-Ziv bound

Works effectively for Gaussian correlations in 0.5-3 bit/sample range

Maintains reasonable computational complexity

Abstract

This paper addresses the problem of coding a continuous random source correlated with another source which is only available at the decoder. The proposed approach is based on the extension of the channel coding concept of syndrome from the discrete into the continuous domain. If the correlation between the sources can be described by an additive Gaussian backward channel and capacity-achieving linear codes are employed, it is shown that the performance of the system is asymptotically close to the Wyner-Ziv bound. Even if such an additive channel is not Gaussian, the design procedure can fit the desired correlation and transmission rate. Experiments based on trellis-coded quantization show that the proposed system achieves a performance within 3-4 dB of the theoretical bound in the 0.5-3 bit/sample rate range for any Gaussian correlation, with a reasonable computational complexity.