A Wyner Ziv codec for correlated vector sources

In this paper we consider lossy coding of vector source x ∈ ℝ^N which is correlated with vector source y ∈ ℝ^N, 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 Karhiinen-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 ofmanageable 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.