A competitive continuous Hopfield neural network for vector quantization in image compression

Vector quantization is a system in which a distortion function is minimized for multidimensional optimization problems. The purpose of such a system is data compression. In this paper, a parallel approach using the competitive continuous Hopfield neural network (CCHNN) is proposed for the vector quantization in image compression. In CCHNN, the codebook design is conceptually considered as a clustering problem. Here, it is a kind of continuous Hopfield network model imposed by the winner-take-all mechanism, working toward minimizing an objective function that is defined as the average distortion measure between any two training vectors within the same class (within-class). It also forward maximizes an objective function defined as the average distortion measure between any two training vectors in separate classes (between-dass). For an image of n training vectors and c objects of interest, the proposed CCHNN would consist of nx c neurons. Each neuron (or training vector) occupies Ix I components of a training vector. In the experimental results, the proposed method shows more promising results after convergence than the generalized Lloyd algorithm. (C) 1999 Elsevier Science Ltd. All rights reserved.