Fast vector quantization using a Bat algorithm for image compression
Abstract Linde Buzo Gray (LBG) a traditional method of vector quantization (VQ) generates a local optimal codebook which results in lower PSNR value. The performance of vector quantization (VQ) depends on the appropriate codebook, so researchers proposed optimization techniques for global codebook generation. Particle swarm optimization (PSO) and Firefly algorithm (FA) generate an efficient codebook, but undergoes instability in convergence when particle velocity is high and non availability of brighter fireflies in the search space respectively. In this paper, we propose a new algorithm called BA LBG which uses Bat Algorithm on initial solution of LBG. It produces an efficient codebook with less computational time and results very good PSNR due to its automatic zooming feature using adjustable pulse emission rate and loudness of bats. From the results, we observed that BA LBG has high PSNR compared to LBG, PSO LBG, Quantum PSO LBG, HBMO LBG and FA LBG, and its average convergence speed is 1.841 times faster than HBMO LBG and FA LBG but no significance difference with PSO.