Minimum-entropy clustering and its application to lossless image coding

The minimum-entropy clustering (MEC) algorithm proposed in this paper provides an optimal method for addressing the non-stationarity of a source with respect to entropy coding. This algorithm clusters a set of vectors (where each vector consists of a fixed number of contiguous samples from a discrete source) using a minimum entropy criterion. In a manner similar to classified vector quantization (CVQ), a given vector is first classified into the class which leads to the lowest entropy and then its samples are coded by the entropy coder designed for that particular class. The MEC algorithm is used in the design of a lossless, predictive image coder. The MEC-based coder is found to significantly outperform the single entropy coder as well as the other popular lossless coders reported in the literature.