Competitive learning vector quantization with evolution strategies for image compression

Abstract

We present a competitive learning vector quantization with evolution strategies for image compression. This technique embeds evolution strategies (ES) into the standard competitive learning vector quantization algorithm (CLVQ). After each iteration during the CLVQ training process, the so-far generated codebook is adjusted by the embedded ES through its recombination, mutation, and selection process. The proposed algorithm can efficiently overcome CLVQ's problems of under-utilization of neurons and initial codebook dependency. The embedding of ES greatly increases the algorithm's capability to avoid local minimums, leading to a global optimization. Experimental results demonstrate that it can obtain significant improvement over CLVQ and other comparable algorithms in image compression applications, especially when it involves larger codebooks.