Conservative Motion Estimation from Multi-image Sequences

Motion estimation in image sequences is a fundamental problem for digital video coding. In this paper, we present a new approach for conservative motion estimation from multi-image sequences. We deal with a system in which most of the motions in the scene are conservative or near-conservative in a certain temporal interval with multi-image sequences. Then a single conservative velocity field in this temporal range can across several successive frames. This system can be proved to be fully constrained or over-constrained when the number of frames is greater than two. A framework with displaced frame difference (DFD) equations, spatial velocity modeling, a nonlinear least-squares model, and Gauss-Newton and Levenberg-Marguardt algorithms for solving the nonlinear system is developed. The proposed algorithm is evaluated experimentally with two standard test image sequences. All successive frames except the last one (used for reference frame) in this conservative system can be synthesized by the motion-compensated prediction and interpolation based on the estimated motion field. This framework can estimate large scale motion field that across more than two successive frames if most of the motions in the scene in the temporal interval are conservative or near-conservative and has better performance than the block matching algorithm.