  Recent years have witnessed an exponential increase in the demand for face
video compression, and the success of artificial intelligence has expanded the
boundaries beyond traditional hybrid video coding. Generative coding approaches
have been identified as promising alternatives with reasonable perceptual
rate-distortion trade-offs, leveraging the statistical priors of face videos.
However, the great diversity of distortion types in spatial and temporal
domains, ranging from the traditional hybrid coding frameworks to generative
models, present grand challenges in compressed face video quality assessment
(VQA). In this paper, we introduce the large-scale Compressed Face Video
Quality Assessment (CFVQA) database, which is the first attempt to
systematically understand the perceptual quality and diversified compression
distortions in face videos. The database contains 3,240 compressed face video
clips in multiple compression levels, which are derived from 135 source videos
with diversified content using six representative video codecs, including two
traditional methods based on hybrid coding frameworks, two end-to-end methods,
and two generative methods. In addition, a FAce VideO IntegeRity (FAVOR) index
for face video compression was developed to measure the perceptual quality,
considering the distinct content characteristics and temporal priors of the
face videos. Experimental results exhibit its superior performance on the
proposed CFVQA dataset. The benchmark is now made publicly available at:
https://github.com/Yixuan423/Compressed-Face-Videos-Quality-Assessment.
