<title>Estimating motion vectorfields with smoothness constraints</title>

Abstract

Motion estimation is a key element in motion-compensated image coding. In the conventional motion estimation algorithms, only image intensity information is exploited to estimate the motion parameters in an image sequence. Motion estimation is usually obtained by searching a best match of intensity signals between two successive images, and the resultant motion vectorfields may be very noisy and not suitable for some specific applications like interframe interpolation. Some simple post-processing techniques like vector smoothing and filtering on noisy vectorfields are not very efficient to improve the reliability of motion estimation. In this paper, a motion estimation algorithm with vectorfield smoothness constraints is proposed. Instead of searching a best match only on interframe intensity signals, the motion estimator aims to search an optimum on both the match of interframe intensity signals and the similarity (or smoothness) of local motion vectors. The experiments indicate that the motion vectorfields obtained by this method are much smoother and more homogeneous than those by the conventional estimation algorithms. The simulations of motion-compensated interframe interpolation further show that the motion parameters estimated by the proposed algorithm are reliable and closely approximate to the real physical motion in the scenes.

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