Vector quantization using the firefly algorithm for image compression

Abstract

The vector quantization (VQ) was a powerful technique in the applications of digital image compression. The traditionally widely used method such as the Linde–Buzo–Gray (LBG) algorithm always generated local optimal codebook. Recently, particle swarm optimization (PSO) was adapted to obtain the near-global optimal codebook of vector quantization. An alterative method, called the quantum particle swarm optimization (QPSO) had been developed to improve the results of original PSO algorithm. The honey bee mating optimization (HBMO) was also used to develop the algorithm for vector quantization. In this paper, we proposed a new method based on the firefly algorithm to construct the codebook of vector quantization. The proposed method uses LBG method as the initial of FF algorithm to develop the VQ algorithm. This method is called FF-LBG algorithm. The FF-LBG algorithm is compared with the other four methods that are LBG, particle swarm optimization, quantum particle swarm optimization and honey bee mating optimization algorithms. Experimental results show that the proposed FF-LBG algorithm is faster than the other four methods. Furthermore, the reconstructed images get higher quality than those generated form the LBG, PSO and QPSO, but it is no significant superiority to the HBMO algorithm.