A Novel Approach for VQ Using a Neural Network, Mean Shift, and Principal Component Analysis

In this paper, a hybrid approach for vector quantization(VQ) is proposed for obtaining the better codebook. It is modified and improved based on the centroid neural network adaptive resonance theory (CNN-ART) and the enhanced LBG (Linde-Buzo-Gray) approaches. Three modules, a neuronal net (NN) based clustering, a mean shift (MS) based refinement, and a principal component analysis (PCA) based seed assignment, are repeatedly utilized. Basically, the seed assignment module generates a new initial codebook to replace the low utilized codewords during the iteration. The NN-based clustering module clusters the training vectors using a competitive learning approach. The clustered results are refined using the mean shift operation. Some experiments in image compression applications were conducted to show the effectiveness of the proposed approach.