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，人脸视频压缩需求呈指数级增长，人工智能的成功将传统混合视频编码的边界进一步拓展。生成式编码方法利用人脸视频的统计先验，已被视为具有合理感知率失真权衡的可行替代方案。然而，从传统混合编码框架到生成模型，空域和时域中失真类型的巨大差异给压缩人脸视频质量评估带来了严峻挑战。本文首次系统性地构建大规模压缩人脸视频质量评估（CFVQA）数据库，旨在理解人脸视频的感知质量与多样化压缩失真。该数据库包含3,240个多压缩等级的压缩人脸视频片段，这些片段源自135个内容多样的源视频，并通过六种代表性视频编解码器处理，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。此外，针对人脸视频压缩，我们开发了人脸视频完整性（FAVOR）指数，该指标考虑人脸视频独特的内容特征与时域先验进行感知质量度量。实验结果表明，该方法在提出的CFVQA数据集上表现优异。该基准现已公开发布于：https://github.com/Yixuan423/Compressed-Face-Videos-Quality-Assessment。