Temporal frame interpolation is an important problem in many areas of modern video processing. However the task of accurate motion estimation for temporal interpolation is an open issue. In this paper we propose a new motion estimation algorithm for motion compensated frame interpolation. The developed algorithm is consistent with the conventional model of true motion and can be considered as a method of local minimization for the optimization problem defined within this model. The evaluation of the algorithm is performed for frame rate upconversion problem. Simulation results demonstrate performance comparable to existing frame rate up-conversion methods.

In this work a novel lossless coding approach for color images is considered. It is based on local-adaptive combination of inter-and intra-component prediction. New context modeling method based on decomposition to binary layers is used for prediction errors. Compression performance of proposed algorithm is proved by experimental results obtained for popular public benchmarks.

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Performance analysis of several state-of-the-art prediction approaches is performed for lossless image compression. To provide this analysis special models of edges are presented: bound-oriented and gradient-oriented approaches. Several heuristic assumptions are proposed for considered intra-and inter-component predictors using determined edge models. Numerical evaluation using image test sets with various statistical features confirms obtained heuristic assumptions.

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