In this paper, we combine a context classification scheme with
adaptive prediction and entropy coding to produce an adaptive
lossless image code?. 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.
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