Correlation estimation for distributed source coding under information exchange constraints

Distributed source coding (DSC) depends strongly on accurate knowledge of correlation between sources. Previous works have reported capacity-approaching code constructions when exact knowledge of correlation is available at the encoder. However, in many applications exact correlation information may not be available, and correlation estimation is necessary. While error in estimation is inevitable, the impact of estimation error on compression efficiency has not been sufficiently studied for the DSC problem. In this paper we study correlation estimation subject to complexity constraints, and its impact on coding efficiency in a DSC framework. In particular, we consider the case where estimation entails information exchange between spatially separate sources and thus correlation estimation is subject to rate constraints. We first derive optimal strategies for information exchange that minimize the rate penalty due to inaccurate estimation, under constraints on the number of bits that can be exchanged between sources. Experimental results show that significant gain is possible by optimally exchanging information. We then derive analytical expressions to quantify the rate penalty, and analyze how rate penalty changes with a priori knowledge of correlation. In addition, we present a model-based estimation method which can achieve more accurate estimation results compared to directly inspecting the data.