Nov. 30, 2023, 11:31 p.m. | Synced

Synced syncedreview.com

A paper titled "Online Video Quality Enhancement with Spatial-Temporal Look-up Tables" introduces a novel method, STLVQE. This research, conducted by a team from Tongji University and Microsoft Research Asia, pioneers the exploration of the online video quality enhancement problem and presents the first method achieving real-time processing speed.


The post Spatial-Temporal Innovation: STLVQE Redefines Real-Time Video Enhancement for an Unmatched Viewing Experience first appeared on Synced.

ai artificial intelligence asia computer vision deep-neural-networks experience exploration innovation look machine learning machine learning & data science microsoft microsoft research ml novel online video paper processing quality real-time real-time processing research spatial speed tables team technology temporal university video video quality video quality enhancement

More from syncedreview.com / Synced

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Data Science Analyst

@ Mayo Clinic | AZ, United States

Sr. Data Scientist (Network Engineering)

@ SpaceX | Redmond, WA