Motion estimation and motion-compensated filtering of video signals

Abstract

The authors consider methods for estimating 2-D motion in time-varying images for application to motion-compensated filtering. The approach is based on the minimization of objective functions that can be interpreted as energies of suitable Gibbs-Markov random fields. A flexible class of cost functions is described that can be applied in a wide variety of specific applications, including the estimation of motion trajectories over several image frames. The issues of minimizing the cost function and applications to motion-compensated filtering are briefly addressed.<>