Near-lossless distributed coding of hyperspectral images using a statistical model for probability estimation

Abstract

In this paper we propose an algorithm for near-lossless compression of hyperspectral images based on distributed source coding (DSC). The encoding is based on syndrome coding of bit-planes of the quantized prediction error of each band, using the same information in the previous band as side information. The practical scheme employs an array of lowdensity parity-check codes. Unlike other existing DSC techniques, the determination of the encoding rate for each data block is completely based on a statistical model, avoiding the need of inter-source communication, as well as of a feedback channel. Moreover, the statistical model allows to estimate the statistics of the currently decoded bit-plane also using the information about the previously decoded ones in the same band; this boosts the performance of the DSC scheme towards the capacity of the conditional entropy of the multilevel (as opposed to binary) source. Experimental results have been worked out using AVIRIS data; a significant performance improvement is obtained with respect to existing DSC and classical techniques, although there is still a gap with respect to the theoretical coding bounds.