Counter-propagation neural network for image compression

Recently, several image compression techniques based on neural network algorithms have been developed. In this paper, we propose a new method for image compression—the modified counterpropagation neural network algorithm, which is a combination of the selforganizing map of Kohonen and the outstar structure of Grossberg. This algorithm has been successfully used in many applications. The modification presented has also demonstrated an interesting performance in comparison with the standard techniques. It was found that at the learning stage we can use any image for a network training (without a significant influence on the net operation) and the compression ratio and quality depend on the size of the basic element (the number of pixels in the cluster) and the amount of error tolerated when processing.