Application of Learning Automata to Image Data Compression
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.
KeywordsCompression Ratio Image Code Conditional Probability Distribution Learning Automaton Huffman Coder
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