Context-dependent distribution shaping and parameterization for lossless image compression

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Abstract
An algorithm class called CaTH (centering and tail handling) is described that is based on predictive coding followed by adaptive binary arithmetic coding. CaTH treats the prediction errors close to zero (i.e., near the center of the error distribution) in a more precise manner than the errors of the `tails' (i.e., errors far from zero). The context model uses error buckets (quantized ranges) of prediction errors. The probability model for the prediction errors uses a histogram for the center. A variety of ways to binarize the tails are studied. The results on the suite of JPEG test images are very encouraging.

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