Neural-network-based compression algorithm for gray scale images

This paper presents an image compression algorithm for gray scale images, based on neural networks. According to this algorithm the image will be first decomposed into Hadamard set of functions and second, the coefficients from the decomposition will be dynamically clustered by a newly proposed dynamic adaptive clustering method (DACM). We show that DACM converges to approximate the optimum solution based on the least sum of squares criterion theoretically and experimentally. We applied the compression method to various gray scale images and show its efficiency in providing high compression rates. In order to show some comparative results for the proposed method, we have chosen the well-known JPEG. Its algorithm has similar structure and therefore is a good basis for comparison. The results from the gray scale images experiments are in favor of the proposed method.