A finite mixture model of geometric distributions for lossless image compression

In this paper, we proposed a new geometric finite mixture model-based adaptive arithmetic coding (AAC) for lossless image compression. Applying AAC for image compression, large compression gains can be achieved only through the use of sophisticated models that provide more accurate probabilistic descriptions of the image. In this work, we proposed to divide the residual image into non-overlapping blocks, and then we model the statistics of each block by a mixture of geometric distributions of parameters estimated through the maximum likelihood estimation using the expectation–maximization algorithm. Moreover, a histogram tail truncation method within each predicted error block is used in order to reduce the number of symbols in the arithmetic coding and therefore to reduce the effect of the zero-occurrence symbols. Experimentally, we showed that using convenient block size and number of mixture components in conjunction with the prediction technique median edge detector, the proposed method outperforms the well known lossless image compressors.