Prediction Capabilities of Boolean and Stack Filters forLossless Image Compression
Multidimensional Systems and Signal Processing
Efficient Algorithms for Lossless Compression of 2D/3D Images
VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
A novel approach for digital waveform compression
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Multimode embedded compression codec engine for power-aware video coding system
IEEE Transactions on Circuits and Systems for Video Technology
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We propose a context-based, adaptive, lossless image codec (CALIC). CALIC obtains higher lossless compression of continuous-tone images than other techniques reported 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 to condition a non-linear predictor and make it adaptive to varying source statistics. The non-linear predictor adapts via an error feedback mechanism. In this adaptation process, CALIC only estimates the expectation of prediction errors conditioned on a large number of 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 sparse context problem. The low time and space complexities of CALIC are attributed to efficient techniques for forming and quantizing modeling contexts.