Scalable vector quantization architecture for image compression

Vector quantization is a popular data compression technique due to its theoretical advantage over scalar quantization which enables exploitation of the dependencies between neighboring samples. However, the complexity of the encoding process imposes certain limitations on the size of the codebook population and/or the dimensions of the processed blocks. In this paper, we show that this complexity can be conveniently distributed as sub-codebooks over general purpose MIMD parallel processors, tb provide almost linearly scalable throughput and flexible configurability. A particular advantage of this approach is that it makes feasible the use of the higher dimensional image blocks and/or larger codebooks, leading to improved coding performance with no penalty in execution speed compared with the original sequential implementation.