Adaptive technique for image compression based on vector quantization using a self-organizing neural network

Abstract

A novel image compression technique employing the self-organized clustering capability of Fuzzy-ART neural network and 2D runlength encoding is presented. Initially the image is divided into 4×4 blocks and the 16 element vectors representing the pixels in the blocks are applied to the Fuzzy-ART network for classification. The image is then represented by the block codes consisting of the sequence of class indices, and the codebook consisting of the class index and their respective gray levels. Further compression is achieved by 2D runlength encoding, making use of the repetitions of the class index in the block codes in x and y directions. By controlling the vigilance parameter of Fuzzy-ART, a reasonable compression of the image without sacrificing the image quality can be obtained. From the experimental results, it can be seen that the proposed method of image compression can be used for image communication systems where large compression ratio is required. An efficient technique for automatic computation of the value of vigilance parameter based on the image characteristics for optimum compression of the image is also presented in this paper. With the introduction of a new class of Fuzzy-ART network, namely Force Class Fuzzy-ART, hardware implementation of the image compression module is made feasible. This architecture constrains the maximum number of classes in the output of the network by forcing the new vectors into one of the closest categories.