Context modeling based on context quantization with application in wavelet image coding
Context modeling is widely used in image coding to improve the compression performance. However, with no special treatment, the expected compression gain will be cancelled by the model cost introduced by high order context models. Context quantization is an efficient method to deal with this problem. In this paper, we analyze the general context quantization problem in detail and show that context quantization is similar to a common vector quantization problem. If a suitable distortion measure is defined, the optimal context quantizer can be designed by a Lloyd style iterative algorithm. This context quantization strategy is applied to an embedded wavelet coding scheme in which the significance map symbols and sign symbols are directly coded by arithmetic coding with context models designed by the proposed quantization algorithm. Good coding performance is achieved.
context modeling quantization wavelet image coding entropy coding