With the explosive increase of User Generated Content (UGC), UGC video quality assessment (VQA) becomes more and more important for improving users' Quality of Experience (QoE). However, most existing UGC VQA studies only focus on the visual distortions of videos, ignoring that the user's QoE also depends on the accompanying audio signals. In this paper, we conduct the first study to address the problem of UGC audio and video quality assessment (AVQA). Specifically, we construct the first UGC AVQA database named the SJTU-UAV database, which includes 520 in-the-wild UGC audio and video (A/V) sequences, and conduct a user study to obtain the mean opinion scores of the A/V sequences. The content of the SJTU-UAV database is then analyzed from both the audio and video aspects to show the database characteristics. We also design a family of AVQA models, which fuse the popular VQA methods and audio features via support vector regressor (SVR). We validate the effectiveness of the proposed models on the three databases. The experimental results show that with the help of audio signals, the VQA models can evaluate the perceptual quality more accurately. The database will be released to facilitate further research.

翻译：随着用户生成内容（UGC）的爆炸式增长，UGC视频质量评估（VQA）对于提升用户体验质量（QoE）愈发重要。然而，现有UGC VQA研究大多仅关注视频的视觉失真，忽略了用户QoE同样依赖于伴随的音频信号。本文首次开展了UGC音视频质量评估（AVQA）的研究。具体而言，我们构建了首个名为SJTU-UAV的UGC AVQA数据库，包含520个真实场景下的UGC音视频（A/V）序列，并通过用户实验获取了这些A/V序列的平均意见得分。随后从音频与视频两个维度分析了SJTU-UAV数据库的内容特性。我们还设计了系列AVQA模型，通过支持向量回归器（SVR）融合主流VQA方法与音频特征。我们在三个数据库上验证了所提模型的有效性。实验结果表明，借助音频信号，VQA模型能够更准确地评估感知质量。该数据库将公开发布以促进后续研究。