A learning-based codebook design for vector quantization using evolution strategies

Abstract

ABSTRACT This paper presents a learning based codebook design algorithm for vector quantization of digital images using evolution strategies (ES). This technique embeds evolution strategies into the standard competitive learning vector quantization algorithm (CLVQ) and efficiently overcomes its problems of under-utilization of neurons and initial codebook dependency. The embedding of ES greatly increases the algo rithm’s capability of avoiding the local minimums, leading to global optimization. Experimental results demonstrate that it can obtain significant improvement over CLVQ and other comparable algorithms in image compression applications. In comparison with the FSLVQ and KSOM algorithms, this new technique is computationally more efficient and requires less training time . Keywords: Vector quantization, neural networks, competitive learning, evolution strategies, codebook generation 1. INTRODUCTION Vector quantization (VQ) which is the exte nsion of scalar quantization to vectors can be considered as an approximation of a set of n-dimensional vectors onto a finite set of representative vectors called a codebook.