Deep Frame Interpolation for Video Compression

Deep neural networks have been recently proposed to solve video interpolation tasks. Given a past and future frame, such networks can be trained to successfully predict the intermediate frame(s). In the context of video compression, these architectures could be useful as an additional inter-prediction mode. Current inter-prediction methods rely on block-matching techniques to estimate the motion between consecutive frames. This approach has severe limitations for handling complex non-translational motions, and is still limited to block-based motion vectors. This paper presents a deep frame interpolation network for video compression aiming at solving the previous limitations, i.e. able to cope with all types of geometrical deformations by providing a dense motion compensation. Experiments with the classical bi-directional hierarchical video coding structure demonstrate the efficiency of the proposed approach over the traditional tools of the HEVC codec.