A new fuzzy reinforcement learning vector quantization algorithm for image compression

Abstract

A new unsupervised fuzzy reinforcement learning vector quantization (FRLVQ) algorithm for image compression based on the combination of fuzzy K-means clustering algorithm and topology knowledge is proposed. In each iteration of reinforcement learning (RL), the size and direction of the movement of a codevector is decided by the overall pair-wise competition between the attraction of each training vector and the repellent force of the corresponding winning codevector. While each training vector only affects the winning codevector in the generalised Lloyd algorithm (GLA) strategy, and only the attraction of training vectors are considered in the fuzzy K-means (FKM) strategy. The competition is measured by the membership function. Simulation results are presented to compare the proposed FRLVQ with GLA and FKM algorithms. It is apparent that FRLVQ has the better quality of codebook design, is very insensitive to the selection of the initial codebook, and relatively insensitive to the choice of learning rate sequences.