A Wyner Ziv Codec for Correlated Vector Sources

Abstract

In this paper we consider lossy coding of vector source x isin R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> which is correlated with vector source y isin R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N </sup> , known at the decoder. The general non-linear mapping between y and x capturing the correlation between the two sources can be approximated through a linear model y = Hx + n in which n is independent of x. We propose a compression scheme, namely, distributed adaptive compression (DAC). The DAC algorithm is inspired by the optimal solution for Gaussian sources and requires computation of the conditional Karhunen-Loeve transform (CKLT) of the data at the encoder. Viewing the dependency model as a fictitious communication channel we utilize linear minimum mean square error (LMMSE) equalizer at the decoder, to convert the original vector source coding problem into a set of manageable scalar source coding problems. Furthermore, inspired by bit loading strategies employed in wireless communication systems, we propose a rate allocation policy which minimizes the decoding error rate under a total rate constraint