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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 e ach vector consists of a xed number of contiguous samples from a discrete source) using a minimum entropy criterion. In a manner similar to Classied V e ctor Quantization (CVQ), a given vector is rst classied into the class which leads to the lowest entropy and then its samples are c o ded by the entropy coder designed for that particular class. In this paper the MEC algorithm is used in the design of a lossless, predictive image coder. The MEC-based coder is found to sigicantly outperform the single entropy coder as well as the other popular lossless coders reported in the literature.

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In this paper, we combine a context classification scheme with adaptive prediction and entropy coding to produce an adaptive lossless image coder. In this coder, we maximize the benefits of adaptivity using both adaptive prediction and entropy coding. The adaptive prediction is closely tied with the classification of contexts within the image. These contexts are defined with respect to the local edge, texture or gradient characteristics as well as local activity within small blocks of the image. For each context an optimal predictor is found which is used for the prediction of all pixels belonging to that particular context. Once the predicted values have been removed from the original image, a clustering algorithm is used to design a separate, optimal entropy coding scheme for encoding the prediction residual. Blocks of residual pixels are classified into a finite number of classes and members of each class are encoded using the entropy coder designed for that particular class. The combination of these two powerful techniques produces some of the best lossless coding results reported so far.

In this a paper a quadtree based method is proposed for classifying blocks of samples in image subbands. Classification of blocks of subband samples according to their energy and variable bit allocation within the subsequent classes has demonstrated considerable gains in coding efficiency. The gains due to classification increase as smaller blocks are used; however, so do the overheads for transmitting the classification information. The quadtree based method proposed in this paper allows for more efficient classification by using variable-sized blocks in order to maximize the classification gain, while maintaining a limit on the classification overheads. Using an efficient quantization scheme such as ACTCQ [5] (Arithmetic and Trellis Coded Quantization), we have been able to demonstrate competitive coding results at low bit-rates.