Learned Bi-Directional Motion Prediction for Video Compression

Motion estimation is a key component to remove the temporal redundancy in video compression. It is well-known that bi-directional motion estimation outperforms sequential motion estimation because of its capability to use both forward and backward reference frames. Previous approaches perform a coarse motion prediction operation to further remove motion spatial redundancy, which heavily rely on the regularity of the motion. However, most motions in natural video sequences are extremely complicated and objects usually move irregularly. To solve this problem, in this paper, we propose a fine motion prediction network by learning an importance map between the bi-directional references. Our designed network can generate more accurate prediction of the motion, yielding less residual. And it is also universal for videos with complex and irregular motions. Both objective and subjective quality results validate the effectiveness of our approach.