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Using non-spatial prior information in block-matching based motion estimation

Due to memory bandwidth limitations and computational complexity considerations in hardware implementations, block matching combined with lscr1 error norm and translational motion model is preferred in motion-estimation algorithms. Performance of this scheme is degraded by noise, compression artifacts, rotation, repeating structures, motion boundaries, zooming, and brightness changes. In this work, we present a Bayesian approach to incorporate prior information into block matching. Hypothesis testing is utilized to choose the most applicable prior motion vector and to compute a prior motion-vector distribution and its precision. Prior distribution is then updated with motion-vector likelihood derived from pixel data to obtain the posterior distribution, which is maximized via a search on the feasible motion-vector space.