A Fish School Search based algorithm for image Channel-Optimized Vector Quantization

Channel-Optimized Vector Quantization (COVQ) is an alternative to Vector Quantization (VQ) in the scenario of transmission over noisy channels. The codebook design is an optimization problem in which a set of vectors must be optimized to represent the signals to be quantized. This paper presents a new approach to COVQ codebook design, which is a challenging optimization problem. The proposed technique embeds the Fish School Search (FSS) as a Swarm Clustering Algorithm to COVQ. Simulation results concerning a Binary Symmetric Channel (BSC) reveal the superiority of the proposed technique over conventional COVQ codebook design in terms of the quality of reconstructed images.