SPS Webinar: Temporal Context Mining for Learned Video Compression
Date: 01-October-2025
Time: 09:00 AM ET (New York Time)
Presenter: Dr. Xihua Sheng
Based on the IEEE Xplore® article under the same title
Published: IEEE Transactions on Multimedia, November 2022.
Download article: Original article will be made publicly available for download on the day of the webinar for 48 hours. ARTICLE LINK
About this topic:
This webinar will present the presenters work on end-to-end learned video compression, with a focus on temporal context mining. Their approach uniquely propagates both reconstructed frames and intermediate features to extract multi-scale temporal contexts, which are then effectively reused across the compression pipeline—including the contextual encoder-decoder, frame generator, and temporal context encoder. By replacing the computationally heavy auto-regressive entropy model, our codec achieves practical encoding/decoding speeds while delivering superior compression performance. Building on this foundation, this webinar will further discuss our advanced inter-prediction techniques specifically optimized for the low-delay scenario that achieve higher compression efficiency, and their novel extensions adapting this method for the random-access scenario.
About the presenter:
Xihua Sheng received B.S. degree in automation from Northeastern University, Shenyang, China, in 2019, and the Ph.D. degree in electronic engineering from University of Science and Technology of China (USTC), Hefei, Anhui, China, in 2024.
He is currently Postdoctoral Fellow in computer science from City University of Hong Kong. His research interests include image/video/point cloud coding, signal processing, and machine learning.
