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.