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Found 5 papers, 1 papers with code
Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression
With the SDD-based motion model and long short-term temporal contexts fusion, our proposed learned video codec can obtain more accurate inter prediction.
Offline and Online Optical Flow Enhancement for Deep Video Compression
In the offline stage, we fine-tune a trained optical flow estimation network with the motion information provided by a traditional (non-deep) video compression scheme, e. g. H. 266/VVC, as we believe the motion information of H. 266/VVC achieves a better rate-distortion trade-off.
VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision
Such compact representations need to be decoded back to pixels before being displayed to humans and - as usual - before being enhanced/analyzed by machine vision algorithms.
Attribute Artifacts Removal for Geometry-based Point Cloud Compression
In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC.
Temporal Context Mining for Learned Video Compression
From the stored propagated features, we propose to learn multi-scale temporal contexts, and re-fill the learned temporal contexts into the modules of our compression scheme, including the contextual encoder-decoder, the frame generator, and the temporal context encoder.
