Differential lossless encoding of images using non-linear predictive techniques

We investigate the problem of constructing a prediction scheme for a given image that results in the minimum zero-order entropy of prediction errors. The problem is formulated as a combinatorial optimization problem. This allows the use of some well known techniques from combinatorial optimization in order to construct heuristic solutions. We describe a few heuristics and give preliminary implementation results. The techniques developed can also be generalized in a straight forward manner to composite source modeling where the data is modeled as an interleaved sequence emanating from k different sub-sources. Although the problems and proposed solutions are described in a strictly deterministic manner, they can also be formulated in a stochastic framework to yield solutions that are valid for a family of images emitted by the same source.