MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos

被引：24
|
作者：
Zhang, Zicheng [1 ]
Wu, Wei [2 ]
Sun, Wei [1 ]
Tu, Danyang [1 ]
Lu, Wei [1 ]
Min, Xiongkuo [1 ]
Chen, Ying [2 ]
Zhai, Guangtao [1 ,3 ]
机构：
[1] Shanghai Jiao Tong Univ, Inst Image Commun & Network Engn, Shanghai, Peoples R China
[2] Alibaba Grp, Hangzhou, Peoples R China
[3] Shanghai Jiao Tong Univ, AI Inst, MoE Key Lab Artificial Intelligence, Shanghai, Peoples R China
来源：
2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR | 2023年
基金：
国家重点研发计划;
关键词：
COMPRESSION; PREDICTION;
D O I：
10.1109/CVPR52729.2023.00174
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
User-generated content (UGC) live videos are often bothered by various distortions during capture procedures and thus exhibit diverse visual qualities. Such source videos are further compressed and transcoded by media server providers before being distributed to end-users. Because of the flourishing of UGC live videos, effective video quality assessment (VQA) tools are needed to monitor and perceptually optimize live streaming videos in the distributing process. In this paper, we address UGC Live VQA problems by constructing a first-of-a-kind subjective UGC Live VQA database and developing an effective evaluation tool. Concretely, 418 source UGC videos are collected in real live streaming scenarios and 3,762 compressed ones at different bit rates are generated for the subsequent subjective VQA experiments. Based on the built database, we develop a Multi-Dimensional VQA (MD-VQA) evaluator to measure the visual quality of UGC live videos from semantic, distortion, and motion aspects respectively. Extensive experimental results show that MD-VQA achieves state-of-the-art performance on both our UGC Live VQA database and existing compressed UGC VQA databases.
引用
收藏
页码：1746 / 1755
页数：10
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