Distributed transform coding via source-splitting

Pradeepa Yahampath

doi:10.1186/1687-6180-2012-78

Abstract

A practical approach to designing distributed transform codes for high-dimensional correlated Gaussian vectors is presented. In this approach, source-splitting based on linear approximations is used to achieve arbitrary rate-pairs, by using only Wyner-Ziv (WZ) quantizers. The optimal bit-allocation among a dependent set of WZ quantizers is found by using a tree-search algorithm. Experimental results obtained with actual designs, which use conditional entropy constrained trellis coded quantizers (CEC-TCQ) and Slepian-Wolf (SW) codes, are presented.

Highlights

Many new applications such as multi-camera imaging systems rely on networks of distributed wireless sensors to acquire signals in the form of high-dimensional vectors [1]
In order practically implement this approach, we introduce the idea of designing conditional entropy constrained trellis-coded quantization (TCQ) (CEC-TCQ) based on analytically found bit-allocations
We present experimental results to demonstrate that practical implementations of split distributed transform coding (SP-distributed transform coding (DTC)) for Gaussian sources can closely approach the performance limits indicated by the optimal SP-distributed KLT (DKLT)

Summary

Introduction

Many new applications such as multi-camera imaging systems rely on networks of distributed wireless sensors to acquire signals in the form of high-dimensional vectors [1]. In such situations, an encoder in each sensor quantizes a vector of observation variables (without exchanging any information with other sensors) and transmits its output to a central processor which jointly decodes all the sources. The strong statistical dependencies among the signals observed by different sensors can be exploited in the decoder to reduce the transmission bit-rate of each sensor This problem, in general, is referred to as distributed (or multiterminal) vector quantization (VQ). TC can be used for distributed VQ when separately observed vectors have both inter-vector

Objectives

Methods

Conclusion