Noise suppression for shape-gain vector quantization by index assignment using ant colony systems

A pioneer work on index assignment using ant colony systems for shape-gain vector quantization is presented in this paper. SGVQ, descended from VQ, is well recognized as a highly efficient compression method, with which encoding speed is greatly improved without serious degradation in image quality. Our work focuses on the transmission of indices in noisy environment. In order to minimize the impact of channel noise, we use ant colony systems to find out a suitable index assignment. With our approach, channel distortion can be substantially reduced without incurring extra cost such as that in error-detection code and error-correction code.