Estimation and compensation of accelerated motion for temporal sequence interpolation

Abstract

Abstract This paper makes two contributions to the area of motion-compensated processing of image sequences. First contribution is the development of a framework for the modeling and estimation of dense 2-D motion trajectories with acceleration. Therefore, Gibbs-Markov models are proposed and linked together by the maximum a posteriori probability (MAP) criterion, and the resulting objective function is minimized using multiresolution deterministic relaxation. Accuracy of the method is demonstrated by measuring the mean-squared error of estimated motion parameters for images with synthetic motion. Second contribution is the demonstration of a significant gain resulting from the use of trajectories with acceleration in motion-compensated temporal interpolation of videoconferencing/videophone images. An even higher gain is demonstrated when the accelerated motion trajectory model is augmented with occlusion and motion discontinuity models. The very good performance of the method suggests a potential application of the proposed framework in the next generation of video coding algorithms.