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 \textbf{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
\underline{M}ulti-\underline{D}imensional \underline{VQA} (\textbf{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.

本文通过构建一个首个主观 UGC 直播视频质量数据库并开发一个有效的评估工具来解决 UGC Live VQA 问题，并基于所构建的数据库开发了一个多维 VQA 评估器以从语义、失真和动态方面衡量 UGC 直播视频的视觉质量。实验结果表明，MD-VQA 在我们的 UGC Live VQA 数据库和现有压缩 UGC VQA 数据库上均实现了最先进的性能。