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30 January 2003 Near-lossless image compression by adaptive prediction: new developments and comparison of algorithms
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This paper describes state-of-the-art approaches to near-lossless image compression by adaptive causal DPCM and presents two advanced schemes based on crisp and fuzzy switching of predictors, respectively. The former relies on a linear-regression prediction in which a different predictor is employed for each image block. Such block-representative predictors are calculated from the original data set through an iterative relaxation-labeling procedure. Coding time are affordable thanks to fast convergence of training. Decoding is always performed in real time. The latter is still based on adaptive MMSE prediction in which a different predictor at each pixel position is achieved by blending a number of prototype predictors through adaptive weights calculated from the past decoded samples. Quantization error feedback loops are introduced into the basic lossless encoders to enable user-defined upper-bounded reconstruction errors. Both schemes exploit context modeling of prediction errors followed by arithmetic coding to enhance entropy coding performances. A thorough performance comparison on a wide test image set show the superiority of the proposed schemes over both up-to-date encoders in the literature and new/upcoming standards.
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Bruno Aiazzi, Luciano Alparone, and Stefano Baronti "Near-lossless image compression by adaptive prediction: new developments and comparison of algorithms", Proc. SPIE 4793, Mathematics of Data/Image Coding, Compression, and Encryption V, with Applications, (30 January 2003);

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