Recent years have witnessed an explosion of user-generated content (UGC)
videos shared and streamed over the Internet, thanks to the evolution of
affordable and reliable consumer capture devices, and the tremendous popularity
of social media platforms. Accordingly, there is a great need for accurate
video quality assessment (VQA) models for UGC/consumer videos to monitor,
control, and optimize this vast content. Blind quality prediction of
in-the-wild videos is quite challenging, since the quality degradations of UGC
content are unpredictable, complicated, and often commingled. Here we
contribute to advancing the UGC-VQA problem by conducting a comprehensive
evaluation of leading no-reference/blind VQA (BVQA) features and models on a
fixed evaluation architecture, yielding new empirical insights on both
subjective video quality studies and VQA model design. By employing a feature
selection strategy on top of leading VQA model features, we are able to extract
60 of the 763 statistical features used by the leading models to create a new
fusion-based BVQA model, which we dub the \textbf{VID}eo quality
\textbf{EVAL}uator (VIDEVAL), that effectively balances the trade-off between
VQA performance and efficiency. Our experimental results show that VIDEVAL
achieves state-of-the-art performance at considerably lower computational cost
than other leading models. Our study protocol also defines a reliable benchmark
for the UGC-VQA problem, which we believe will facilitate further research on
deep learning-based VQA modeling, as well as perceptually-optimized efficient
UGC video processing, transcoding, and streaming. To promote reproducible
research and public evaluation, an implementation of VIDEVAL has been made
available online: https://github.com/tu184044109/VIDEVAL_release.

通过在视频质量评估领域进行广泛的评估，该论文提出了一种新的基于融合的视频质量评估模型 VIDEVAL，有效平衡了 VQA 性能和效率，并在考虑了更少的统计特征（即 60 个统计特征）的情况下取得了最先进的性能。