Reversible multiresolution image coding based on adaptive lifting

We first calculate the optimal predictors of a lifting scheme in the general n-dimensional case. Then, we apply these optimal predictor filters with corresponding update filters for the lossless compression of still images using simple row-column sampling. The efficiency of the linear predictor is enhanced by nonlinear means, namely by adaptive-length postprocessing. The resulting filter bank in effect adapts to the features of the area of the image being processed and in this way it is shown to achieve superior performance than even the most efficient known lossless compression methods capable of progressive transmission.