Parametric OBMC for Pixel-Adaptive Temporal Prediction on Irregular Motion Sampling Grids

Abstract

This paper adapts overlapped block motion compensation (OBMC) to suit variable block-size motion partitioning. The motion vectors (MVs) for various partitions are formalized as motion samples taken on an irregular grid. From this viewpoint, determining OBMC weights to associate with these samples becomes an under-determined problem since a distinct solution has to be sought for each prediction pixel. In this paper, we tackle this problem by expressing the optimal weights in closed form based on parametric signal assumptions. In particular, the computation of this solution requires only the geometric relations between the prediction pixel and its nearby block centers, leading to a generic framework capable of reconstructing temporal predictors from any irregularly sampled MVs. A modified implementation is also proposed to address the MV location uncertainty and to reduce computational complexity. Experimental results demonstrate that our scheme performs better than similar previous works, and when compared to the recently proposed Quadtree-based adaptive loop filter and enhanced adaptive interpolation filter, show a comparable gain. Furthermore, the combination of it with either of them gives a combined effect that is almost the sum of their separate improvements.