Image compression with Generalized Lifting and partial knowledge of the signal pdf

In this paper we deal with the use of generalized lifting (GL) for lossy image compression. We have demonstrated in [9] the potential of the method for coding assuming complete knowledge of the pdf of the image to encode. Here, we move towards a realistic scheme that does not assume complete knowledge of the pdf. We show that a multiscale GL produces interesting results even if the pdf of the image to encode is only partially known. We target the compression of a given image class and compute an estimate of the image class pdf. This pdf is available at both encoder and decoder. A decision algorithm minimizes the overhead produced by the difference between the class pdf and the image pdf. This algorithm also removes ambiguities in the decoding process. The encoding strategy is completed using an arithmetic encoder. Results exhibit improvements over the state of the art.