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Context-based, adaptive, lossless image coding
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DOI：10.1109/26.585919.png)
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We propose a context-based, adaptive, lossless image codec (CALIC). The codec obtains higher lossless compression of continuous-tone images than other lossless image coding techniques in the literature, This high coding efficiency is accomplished with relatively low time and space complexities, CALIC puts heavy emphasis on image data modeling. A unique feature of CALIC is the use of a large number of modeling contexts (states) to condition a nonlinear predictor and adapt the predictor to varying source statistics, The nonlinear predictor can correct itself via an error feedback mechanism by learning from its mistakes under a given context in the past. In this learning process, CALIC estimates only the expectation of prediction errors conditioned on a large number of different contexts rather than estimating a large number of conditional error probabilities, The former estimation technique can afford a large number of modeling contexts without suffering from the context dilution problem of insufficient counting statistics as in the latter approach, nor from excessive memory use, The low time and space complexities are also attributed to efficient techniques for forming and quantizing modeling contexts.
Keyword:
adaptive prediction
entropy coding
image compression
statistical context modeling
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IF:
8.3
论文数:
1.2W
被引数:
3.6W
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Lossless compression of continuous-tone images via context selection, quantization, and modeling
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