Distributed Lossy Source Coding Using Real-Number Codes

TL;DR

This paper introduces a novel framework for lossy distributed source coding using real-number codes, allowing more realistic modeling of source correlation and improved reconstruction quality.

Contribution

It proposes a new approach that quantizes compressed sources before binning, enhancing correlation modeling and error correction in continuous-valued sources.

Findings

Reconstructed signals have lower mean squared error.

The framework effectively models correlation in continuous sources.

DFT codes are used for encoding and decoding.

Abstract

We show how real-number codes can be used to compress correlated sources, and establish a new framework for lossy distributed source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlation between continuous-valued sources more realistically and correct quantization error when the sources are completely correlated. The encoding and decoding procedures are described in detail, for discrete Fourier transform (DFT) codes. Reconstructed signal, in the mean squared error sense, is seen to be better than that in the conventional approach.