Comparative Performance Analysis of Optimization Techniques on Vector Quantization for Image Compression

Linde-Buzo-Gray (LBG) Vector Quantization (VQ), technically generates local codebook after many runs on different sets of training images for image compression. The key role of VQ is to generate global codebook. In this paper, we present comparative performance analysis of different optimization techniques. Firefly and Cuckoo search generate a near global codebook, but undergoes problem when non-availability of brighter fireflies and convergence time is very high respectively. Hybrid Cuckoo Search (HCS) algorithm was developed and tested on four benchmark functions, that optimizes the LBG codebook with less convergence rate by taking McCulloch's algorithm based levy flight and variant of searching parameters. Practically, we observed that Bat algorithm (BA) peak signal to noise ratio is better than LBG, FA, CS and HCS in between 8 to 256 codebook sizes. The convergence time of BA is 2.4452, 2.734 and 1.5126 times faster than HCS, CS and FA respectively.