Fractal-based gradient-match and side-match vector quantization for image coding
Hsuan-Ting Chang, Tuan Y. Han · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
In this paper, we propose the fractal-based gradient match vector quantizers (FGMVQs) and the fractal-based side match vector quantizers (FSMVQs) for the image coding framework. The proposed schemes are based upon the non-iterative fractal block coding (FBC) technique and the concepts of the gradient match vector quantizers (GMVQs) and the side match vector quantizers (SMVQs). Unlike the ordinary GMVQs and SMVQs, the super codebooks in the proposed FGMVQs and FSMVQs are generated from the affine-transformed domain blocks in the non-iterative FBC technique. The codewords in the state codebook are dynamically extracted from the super codebook with the side-match and gradient-match criteria. The redundancy in affine-transformed domain blocks is greatly reduced and the compression ratio can be significantly increased. Our simulation results show that about 10% - 20% bit rates in the non-iterative FBC techniques are saved by using the proposed FGMVQs and FSMVQs.