A new dynamic finite-state vector quantization algorithm for image compression

The picture quality of conventional memory vector quantization techniques is limited by their supercodebooks. This paper presents a new dynamic finite-state vector quantization (DFSVQ) algorithm which provides better quality than the best quality that the supercodebook can offer. The new DFSVQ exploits the global interblock correlation of image blocks instead of local correlation in conventional DFSVQs. For an input block, we search the closest block from the previously encoded data using the side-match technique. The closest block is then used as the prediction of the input block, or used to generate a dynamic codebook. The input block is encoded by the closest block, dynamic codebook or supercodebook. Searching for the closest block from the previously encoded data is equivalent to expand the codevector space; thus the picture quality achieved is not limited by the supercodebook. Experimental results reveal that the new DFSVQ reduces bit rate significantly and provides better visual quality, as compared to the basic VQ and other DFSVQs.