New Methods for Lossless Image Compression Using Arithmetic Coding

Abstract We give a new paradigm for lossless image compression, with four modular components: pixel sequence, prediction, error modeling and coding. We present two new methods (called MLP and PPPM) for lossless compression, both involving linear prediction, modeling prediction errors by estimating the variance of a Laplace distribution, and coding using arithmetic coding applied to precomputed distributions. The MLP method is both progressive and parallelizable. We give results showing that our methods perform significantly better than other currently used methods for lossless compression of high resolution images, including the proposed JPEG standard. We express our results both in terms of the compression ratio and in terms of a useful new measure of compression efficiency, which we call compression gain.