The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied

for the lossless compression of still images using rst quincunx sampling and then simple row-column

sampling. In each case, the e ciency of the linear predictors is enhanced nonlinearly. Directional post-

processing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both

methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely e cient

image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet

coe cients 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.

-　　The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied

for the lossless compression of still images using rst quincunx sampling and then simple row-column

sampling. In each case, the e ciency of the linear predictors is enhanced nonlinearly. Directional post-

processing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both

methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely e cient

image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet

coe cients 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.