Motion-compensated frame rate conversion using an accelerated motion model

Abstract

Frame rate conversion requires interpolation of image frames at time instances, where the original sequence has not been sampled. This can be done with high quality only by means of motion-compensated algorithms, therefore a knowledge of the motion present in the sequence is essential. This motion information has to be determined from the image sequence itself. Most motion estimation algorithms use only a simple motion model, and assume linear constant speed motion. Our contribution is the development of an algorithm for modeling and estimation of accelerated motion trajectories, based on a more general second order motion model, and its application to frame rate conversion. The parameters of the accelerated motion are determined from two consecutive motion fields, estimated from three consecutive image frames using a multiresolution pel-recursive Wiener-based motion estimation algorithm. The algorithm was successfully tested on artificial image sequences with synthetic motion as well as on natural real-life videophone and videoconferencing sequences.