Classi ed Adaptive Prediction and Entropy Coding for Lossless Coding of Images

Natural images often consist of many distinct regions with individual characteristics. Adaptive image coders exploit this feature of natural images to obtain better compression results. In this paper, we propose a classi cationbased scheme for both adaptive prediction and entropy coding in a lossless image coder. In the proposed coder, blocks of image samples (in PCM domain) are classi ed to select an appropriate linear predictor from nite set of predictors. Once the predictors have been determined, the image is DPCM coded. A second classi cation is then performed to select a suitable entropy coder for each block of DPCM samples. These classi cation schemes are designed using two separate clustering procedures which attempt to minimize the bit-rate of the encoded image. The coder was tested on a set of monochrome images and was found to produce very promising results.