Gradient-based algorithms for block-oriented MAP estimation of motion and application to motion-compensated temporal interpolation

A study of block-oriented motion estimation algorithms is presented, and their application to motion-compensated temporal interpolation is described. In the proposed approach, the motion field within each block is described by a function of a few parameters that can represent typical local motion vector fields.. A probabilistic formulation is then used to develop maximum-likelihood (ML) and maximum a posteriori probability (MAP) estimation criteria. The MAP criterion takes into account the dependence of the motion fields in adjacent blocks. A procedure for minimizing the resulting objective function based on the Gauss-Newton algorithm is presented. The use of multiresolution techniques, essential for satisfactory performance, is discussed. Experimental results evaluating the algorithms for the task of motion-compensated temporal interpolation are presented. The relative complexity of the algorithms is also discussed