  Blind or no-reference video quality assessment of user-generated content
(UGC) has become a trending, challenging, heretofore unsolved problem. Accurate
and efficient video quality predictors suitable for this content are thus in
great demand to achieve more intelligent analysis and processing of UGC videos.
Previous studies have shown that natural scene statistics and deep learning
features are both sufficient to capture spatial distortions, which contribute
to a significant aspect of UGC video quality issues. However, these models are
either incapable or inefficient for predicting the quality of complex and
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 (SOTA) models but with orders-of-magnitude faster runtime.
RAPIQUE combines and leverages the advantages of both quality-aware scene
statistics features and semantics-aware deep convolutional features, allowing
us to design the first general and efficient spatial and temporal (space-time)
bandpass statistics model for video quality modeling. Our experimental results
on recent large-scale UGC video quality databases show that RAPIQUE delivers
top performances on all the datasets at a considerably lower computational
expense. We hope this work promotes and inspires further efforts towards
practical modeling of video quality problems for potential real-time and
low-latency applications. To promote public usage, an implementation of RAPIQUE
has been made freely available online: \url{https://github.com/vztu/RAPIQUE}.
