PSO Algorithm Applied to Codebook Design for Channel-Optimized Vector Quantization

Abstract

Vector quantization (VQ) has been used in signal compression systems. However, in the scenario of image transmission, VQ is very sensitive to channel errors. An approach to decrease such sensitivity is channel-optimized vector quantization (COVQ), which involves VQ codebook design taking into account the characteristics of the channel. In the present work, particle swarm optimization (PSO) is applied to codebook design for COVQ. Simulation results are presented for a variety of bit error rates of a binary symmetric channel (BSC) and reveal the effectiveness of the method in decreasing visual impairment by blocking artifacts in the reconstructed images, overperforming conventional COVQ codebook design in terms of peak signal to noise ratio of the reconstructed images for approximately 90% of exhaustive evaluations of image transmission over BSC.Keywords-image vector quantization, channel-optimized vector quantization, particle swarm optimization, signal processing.

A Codebook Compression Method for Vector Quantization Algorithm

“…Some studies focused on the learning of the codebook of VQ using machine learning [4] and convolutional neural networks (CNN) [5]. Another approach reported the usage of evolutionary algorithms (EA) [6][7][8] for better codebook design.…”