APPLICATION OF LEARNING AUTOMATA TO IMAGE DATA COMPRESSION.

Abstract

A novel approach to image data compression is proposed which uses a stochastic learning automaton to predict the conditional probability distribution of the adjacent pixels. These conditional probabilities are used to code the gray level values using a Huffman coder. The system achieves a 4/1. 7 compression ratio. This performance is achieved without any degradation to the received image.