Algorithms and architecture for image adaptive vector quantization

In this paper, we present two algorithms for vector quantization of images and an architecture to implement these algorithms. In vector quantization (VQ), the image vectors are usually coded with an “universal” codebook, however, for a given image, only a subset of the codewords in the universal codebook may be needed. This means that effectively a smaller label size can be employed at the expense of a small overhead information to indicate to the receiver the codewords used. Simulation results demonstrate the superior coding performance of this technique. VQ using an universal codebook (VQUC) is computationally less demanding but its performance is poor for images outside the training sequence. Image adaptive techniques, where new codebooks are generated, for each input image (VQIAC) can improve the performance but at the cost of increased computational complexity. A technique which combines the advantages of VQUC and VQIAC is presented in this paper. Simulation results demonstrate that the technique gives a coding performance close to that obtained with image adaptive VQ at a substantially reduced computational complextiy. A systolic array architecture to implement the algorithms in real-time is also presented. The regular and iterable structure makes possible the VLSI implementation of the architecture.