Deep motion estimation for parallel inter-frame prediction in video compression
This addresses video compression efficiency for applications requiring low bitrates, offering a novel method but with incremental improvements over existing neural approaches.
The paper tackled the problem of video compression by replacing optical flow-based motion estimation with learned binary motion codes that capture complex motion, resulting in outperforming H.264 and H.265 codecs at low bitrates.
Standard video codecs rely on optical flow to guide inter-frame prediction: pixels from reference frames are moved via motion vectors to predict target video frames. We propose to learn binary motion codes that are encoded based on an input video sequence. These codes are not limited to 2D translations, but can capture complex motion (warping, rotation and occlusion). Our motion codes are learned as part of a single neural network which also learns to compress and decode them. This approach supports parallel video frame decoding instead of the sequential motion estimation and compensation of flow-based methods. We also introduce 3D dynamic bit assignment to adapt to object displacements caused by motion, yielding additional bit savings. By replacing the optical flow-based block-motion algorithms found in an existing video codec with our learned inter-frame prediction model, our approach outperforms the standard H.264 and H.265 video codecs across at low bitrates.