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.

翻译：近年来，面部视频压缩需求呈指数级增长，而人工智能的成功将传统混合视频编码的边界进一步拓展。基于生成式编码的方法利用面部视频的统计先验，已成为具备合理感知率失真权衡的前景方案。然而，从传统混合编码框架到生成式模型，空间和时间域中失真类型的巨大差异为压缩面部视频质量评估（VQA）带来了重大挑战。本文首次系统性地构建了大规模压缩面部视频质量评估（CFVQA）数据库，旨在深入理解面部视频的感知质量与多样化压缩失真。该数据库包含3,240个多压缩级别的面部视频片段，这些片段源自135个内容各异的原始视频，并采用六种代表性视频编解码器（包括两种基于混合编码框架的传统方法、两种端到端方法及两种生成方法）生成。此外，我们针对面部视频压缩开发了面部视频完整性（FAVOR）指数，该指数通过考虑面部视频独特的内容特征与时域先验来度量感知质量。实验结果表明，该方法在提出的CFVQA数据集上表现卓越。该基准测试现已公开访问，网址为：https://github.com/Yixuan423/Compressed-Face-Videos-Quality-Assessment。