Feasibility of self organization in image compression

Abstract

The development of a more efficient solution to the problem of image data compression for real-time situations is addressed. It is proposed that real-time image data compression can be achieved by using a neural network model based on an unsupervised learning method called self-organization. An attempt is made to determine the feasibility of using Kohonen-type networks and to compare this with other approaches using relevant performance indicators.<>