Bayesian frame interpolation by fusing multiple motion-compensated prediction frames
For video frame rate up-conversion, new frames need to be interpolated along motion trajectories and inserted between the original adjacent frames. This paper proposes a Bayesian frame interpolation strategy, which considers both the spatial (i.e. intra-frame) correlation model and the temporal (i.e. inter-frame) correlation model after motion compensation. Different from the conventional schemes that perform interpolation using only one estimated motion vector field (MVF), the proposed strategy estimates multiple MVFs. To cope with different scales of frame contents, we generate the multiple MVFs through variable block size motion estimation. A criterion is adopted to estimate reliability of the motion compensated prediction frames from different motion fields. Experimental results demonstrate that the proposed strategy improves the interpolation performance remarkably.