Genetic fuzzy entropy-constrained vector quantization

A novel variable-rare vector quantizer (VQ) design algorithm using both gen eric and fuzzy clustering techniques is presented. The algorithm, termed ge netic fuzzy entropy-constrained VQ (GFECVQ) design algorithm, has a superio r rate-distortion performance than that of the existing variable-rate VQ de sign algorithms. The algorithm utilizes fuzzy clustering technique to enhan ce the rate-distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance a gainst the selection of initial parameters. Simulation results demonstrate that the FECVQ can be an effective alternative for the design of variable-r ate VQs.