Efficient side-information context description for context-based adaptive entropy coders

This paper discusses with efficient side-information context description for context-based adaptive entropy coders. The design of context-based adaptive entropy coders is nontrivial due to the balance that must be struck between the benefits associated with using a large number of conditioning classes, or contexts, and the penalties resulting from data dilution. Recently an iterative algorithm is proposed, that begins with a large number of conditioning classes and then uses a clustering procedure to group them into a desired size. This method performs very well in contrast to the more usual approach of defining contexts in an ad-hoc manner. In this paper, two techniques were efficiently describing the context book one is coarse context quantization, and another one is classification map. The side information is decreased by 90%-95% when the classification map strategy is applied. It can be further compressed if the coarse context quantization method is used. It is believed that these techniques have great potential for improving the performance of data compression algorithms.