Near-lossless image compression by relaxation-labelled prediction
Signal Processing - Image and Video Coding beyond Standards
Virtually lossless compression of astrophysical images
EURASIP Journal on Applied Signal Processing
Minimum Mean Absolute Error Predictors for Lossless Image Coding
IEICE - Transactions on Information and Systems
Hierarchical lossless image coding using cellular neural network
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
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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 classification-based scheme for both adaptive prediction and entropy coding in a lossless image coder. In the proposed coder, blocks of image samples (in the PCM domain) are classified to select an appropriate linear predictor from finite set of predictors. Once the predictors have been determined, the image is DPCM coded. A second classification is then performed to select a suitable entropy coder for each block of DPCM samples. These classification 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.