PROJECT TITLE :
PSO Algorithm Applied to Codebook Design for Channel-Optimized Vector Quantization
Vector quantization (VQ) has been used in signal compression systems. But, in the situation of image transmission, VQ is terribly sensitive to channel errors. An approach to decrease such sensitivity is channel-optimized vector quantization (COVQ), which involves VQ codebook style taking under consideration the characteristics of the channel. In the current work, particle swarm optimization (PSO) is applied to codebook design for COVQ. Simulation results are presented for a selection of bit error rates of a binary symmetric channel (BSC) and reveal the effectiveness of the strategy in decreasing visual impairment by blocking artifacts within the reconstructed pictures, overperforming typical COVQ codebook style in terms of peak signal to noise ratio of the reconstructed pictures for about 90p.c of exhaustive evaluations of image transmission over BSC.
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