no code implementations • 29 Jan 2024 • Xihua Sheng, Li Li, Dong Liu, Houqiang Li
With the SDD-based motion model and long short-term temporal contexts fusion, our proposed learned video codec can obtain more accurate inter prediction.
no code implementations • 11 Jul 2023 • Chuanbo Tang, Xihua Sheng, Zhuoyuan Li, Haotian Zhang, Li Li, Dong Liu
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.
no code implementations • 19 Jun 2023 • Xihua Sheng, Li Li, Dong Liu, Houqiang Li
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.
no code implementations • 1 Dec 2021 • Xihua Sheng, Li Li, Dong Liu, Zhiwei Xiong
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.
1 code implementation • 27 Nov 2021 • Xihua Sheng, Jiahao Li, Bin Li, Li Li, Dong Liu, Yan Lu
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.