<title>Motion-compensated interpolation using trajectories with acceleration</title>

Abstract

This paper is primarily concerned with motion-compensated interpolation of video sequences using multiple images. Due to the extended temporal support of such motion compensation, linear (constant-velocity) trajectory model is often inappropriate, for example due to insufficient temporal sampling. Recently, we have proposed a quadratic (constant-acceleration) trajectory model and a framework for the computation of its parameters. The approach is based on Markov random field models that lead to a regularized formulation solved by multiresolution deterministic relaxation. In this paper, we demonstrate advantages of using accelerated motion over linear trajectories in a plausible application using natural data. We apply the estimated trajectories to motion-compensated interpolation over multiple frames of progressive and interlaced video sequences. The experimental results for `Miss America' and `Femme et arbre' (interlaced) show, respectively, a 4 and 2 dB average improvement in the PSNR of the reconstruction error when quadratic trajectories are used instead of the linear ones. It is interesting to note that in `Miss America' the most significant improvements can be observed in the area of the mouth and the eyes which are in fact likely to exhibit acceleration. We envisage an application of the proposed method to post-processing in very low bit rate video coding.