Vector quantization with hierarchical classification of sub-blocks

摘要

A hierarchical classified vector quantization (HCVQ) method is described. In this method, the image is coded in several steps, starting with a relatively large block size, and successively dividing the block into smaller sub-blocks in a quad-tree fashion. The initial block is first vector quantized in the normal way. Classified vector quantization is then performed for its sub-blocks using the vector index of the initial block, i.e. rough information of the image, and the location of the sub-block within the initial block as classifiers. The coding proceeds in a similar way, adding more information of the fine details at each level. The method is found to be effective and to give a good subjective quality. It is also simple to implement, leading to coding speeds typical to a tree search VQ.