Adaptive algorithms for image coding using vector quantization

Abstract

Vector quantization (VQ) is a powerful technique for low bit-rate image coding. The two basic steps in vector quantization are codebook generation and encoding. In VQ, a universal codebook is usually designed from a training set of vectors drawn from many different kinds of images. The coding performance of vector quantization can be improved by employing adaptive techniques. The applicability of vector quantization is, however, limited by its computational complexity. In this paper, we propose two adaptive algorithms for image vector quantization which provide a good compromise between coding performance and computational complexity resulting in a very good performance at a reduced complexity. In the first algorithm, a subset of codewords from a universal codebook is used to code an image. The second algorithm starts with the reduced codebook and requires one iteration to adapt the codewords to the image to be coded. Simulation results demonstrate the gains in coding performance and the savings in computational complexity.