Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding

The optimal predictors of a lifting scheme in the general n-dimensional cas e are obtained and applied for the lossless compression of still images usi ng first quincunx sampling and then simple row-column sampling. In each cas e, the efficiency of the linear predictors is enhanced nonlinearly, Directi onal postprocessing is used in the quincunx case, and adaptive-length postp rocessing in the row-column case. Both methods are seen to perform well. Th e resulting nonlinear interpolation schemes achieve extremely, efficient im age decorrelation. We further investigate context modeling and adaptive ari thmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.