Dynamic finite state VQ of colour images using stochastic learning

摘要

A dynamic finite state vector quantization technique for colour image compression is proposed. The method efficiently exploits both the statistical redundancy between the colour components of a pixel and the high correlations between adjacent pixels. The image is compressed losslessly as compared with colour quantized index image. Experimental results show that it can significantly reduce the storage requirement while maintaining excellent image quality. An improved technique which incorporates learning automata is devised. The proposed method can dynamically adapt to the statistics of the input image based on the previous encoding experience. The improved technique can achieve higher coding efficiency. It is also compared with different adaptive lossless compression methods and the results are encouraging.