Context formation by mutual information maximization
In this paper, the problem of how to form the contexts for context-based entropy coding is studied. The mutual information (MI) between the context and the encoded data is used to measure the context optimality. The MI decreases when contexts are combined together. Given a desired number of contexts, an algorithm is proposed for finding the set of contexts by iteratively combining the pairs that give the minimum MI reduction. The proposed algorithm is applied to form the contexts for the zero coding (ZC) primitive of the JPEG2000 image compression standard. Experimental results show that the number of contexts used as part of the standard can be reduced without loss in the coding performance.