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Abstract:We propose a hyperspectral image compressor called BH which considers its input image as being partitioned into square blocks, each lying entirely within a particular band, and compresses one such block at a time by using the following steps: first predict the block from the corresponding block in the previous band, then select a predesigned code based on the prediction errors, and finally encode the predictor coefficient and errors. Apart from giving good compression rates and being fast, BH can provide random access to spatial locations in the image.We hypothesize that BH works well because it accommodates the rapidly changing image brightness that often occurs in hyperspectral images. We also propose an intraband compressor called LM which is worse than BH, but whose performance helps explain BH's performance.

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This paper presents a probabilistic model for use in lossless image compressors. For each pixel the model provides a conditional distribution, which in the most simple case is a discretized gaussian. The mean and variance of this gaussian are determined by using that pixel's neighbors to search a tree to find an autoregressive model, which is then applied to those same neighbors. Finally, arithmetic coding transmits the pixel. This paper also shows how to design this tree and find the distribution parameters associated with each leaf.