Distributed Predictive Coding For Spatio-Temporally Correlated Sources

This paper investigates distributed predictive coding of correlated sources with memory, which are communicated to a central receiver. This is the setting typically encountered in sensor networks. While source memory may be exploited by distributed coding of large source blocks (vectors), the growth in complexity (and delay) is often unacceptable in practice, hence the interest in a low complexity predictive approach. We first consider the inherent "conflict" between distributed and predictive coding due to the impact of distributed quantization on the prediction loop. This is coupled with the effects of closed loop prediction, which destabilize standard Lloyd-like code design methods. An iterative algorithm is derived, which optimizes the overall system while imposing zero decoder drift due to distributed quantization. The approach circumvents convergence and stability issues of traditional predictive quantizer design by employing an "asymptotic closed loop" framework which is adapted for distributed predictive system design. The scheme efficiently utilizes both the temporal and inter-source correlations and subsumes as extreme special cases both separate source predictive coding, and distributed coding of memoryless correlated sources.