A model-based approach to correlation estimation in wavelet-based distributed source coding with application to hyperspectral imagery

In many practical distributed source coding (DSC) applications correlation information has to be obtained at the encoder in order to determine the encoding rate. Coding efficiency depends strongly on the accuracy of this correlation estimation, which often has to be performed under rate and complexity constraints. In this paper we focus on correlation estimation for wavelet-based DSC. We extend our previously proposed model-based estimation techniques, which provided accurate estimates of bit-plane level correlation under rate constraints, in the simple case where bit-planes are generated from the binary representation of the sources. To extend the model-based approach to wavelet-based DSC, we need to address two issues. Firstly, in order to improve coding efficiency, bit-planes are typically generated by more sophisticated algorithms in wavelet-based DSC (e.g., by deciding on the bitplane scan order based on coefficient "significance"), which makes model-based estimation more challenging. Secondly, certain wavelet subbands may not have enough coefficients for reliable model estimation, so that model-based techniques alone may not be sufficiently accurate. We propose solutions to these problems and, using a DSC-based hyperspectral image system as example, we demonstrate that model-based estimation can lead to efficient system implementation with lower computational and data exchange requirements, and improved parallelism, while incurring only small degradation in coding efficiency.