Paper Info
Title
Efficient User-Generated Video Quality Prediction
Abstract
Blind video quality assessment of user-generated content (UGC) has become a trending, challenging, unsolved problem. Accurate and efficient video quality predictors suitable for this content are thus in great demand to achieve intelligent analysis and processing of UGC videos. However, previous video quality models are either incapable or inefficient for predicting the quality of complex, diverse UGC videos in practical applications. Here we introduce an effective and efficient video quality model for UGC content, which we dub the Rapid and Accurate Video Quality Evaluator (RAPIQUE), which we show performs comparably to state-of-the-art models but with orders-of-magnitude faster runtime. Our experimental results on recent large-scale UGC video quality databases show that RAPIQUE delivers top performances on all datasets at a considerably lower computational expense. An implementation of RAPIQUE is online: https://github.com/vztu/RAPIQUE.
Year
DOI
Venue
2021
10.1109/PCS50896.2021.9477483
2021 Picture Coding Symposium (PCS)
Keywords
DocType
ISSN
Video quality assessment,scene statistics,temporal,perceptual quality,user-generated content
Conference
2330-7935
ISBN
Citations 
PageRank 
978-1-6654-3078-4
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Tu Zhengzhong151.08
Chen Chia-Ju252.10
Yilin Wang300.68
Neil Birkbeck414116.44
Balu Adsumilli5168.19
Alan C. Bovik600.34