# Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method

arXiv: https://arxiv.org/abs/2304.07056 · HTML: https://mlsanity.com/paper/2304.07056/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 108 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 27 of 27)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [The Perception-Distortion Tradeoff](https://mlsanity.com/paper/1711.06077/) (2017)
- [DVC: An End-to-end Deep Video Compression Framework](https://mlsanity.com/paper/1812.00101/) (2018)
- [Few-shot Video-to-Video Synthesis](https://mlsanity.com/paper/1910.12713/) (2019)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [X2Face: A network for controlling face generation by using images, audio, and pose codes](https://mlsanity.com/paper/1807.10550/) (2018)
- [PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration](https://mlsanity.com/paper/2007.12142/) (2020)
- [FVC: A New Framework towards Deep Video Compression in Feature Space](https://mlsanity.com/paper/2105.09600/) (2021)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [Large-Scale Study of Perceptual Video Quality](https://mlsanity.com/paper/1803.01761/) (2018)
- [YouTube UGC Dataset for Video Compression Research](https://mlsanity.com/paper/1904.06457/) (2019)
- [SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness](https://mlsanity.com/paper/2003.09373/) (2020)
- [Face Image Quality Assessment: A Literature Survey](https://mlsanity.com/paper/2009.01103/) (2020)
- [Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model](https://mlsanity.com/paper/2006.13560/) (2020)
- [SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance](https://mlsanity.com/paper/2103.05977/) (2021)
- [FaceQnet: Quality Assessment for Face Recognition based on Deep Learning](https://mlsanity.com/paper/1904.01740/) (2019)
- [VideoSet: A Large-Scale Compressed Video Quality Dataset Based on JND Measurement](https://mlsanity.com/paper/1701.01500/) (2017)
- [Low Bandwidth Video-Chat Compression using Deep Generative Models](https://mlsanity.com/paper/2012.00328/) (2020)
- [IFQA: Interpretable Face Quality Assessment](https://mlsanity.com/paper/2211.07077/) (2022)
- [Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model](https://mlsanity.com/paper/2207.04904/) (2022)
- [DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space](https://mlsanity.com/paper/2208.03323/) (2022)
- [SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment](https://mlsanity.com/paper/1804.04813/) (2018)
- [Neural Face Video Compression using Multiple Views](https://mlsanity.com/paper/2203.15401/) (2022)
- [User-generated Video Quality Assessment: A Subjective and Objective Study](https://mlsanity.com/paper/2005.08527/) (2020)

## Built on by (most cited first, 14 of 14)

- [Generative Face Video Coding Techniques and Standardization Efforts: A Review](https://mlsanity.com/paper/2311.02649/) (2023)
- [Generative Visual Compression: A Review](https://mlsanity.com/paper/2402.02140/) (2024)
- [Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wild](https://mlsanity.com/paper/2409.05540/) (2024)
- [FVQ: A Large-Scale Dataset and an LMM-based Method for Face Video Quality Assessment](https://mlsanity.com/paper/2504.09255/) (2025)
- [AGHI-QA: A Subjective-Aligned Dataset and Metric for AI-Generated Human Images](https://mlsanity.com/paper/2504.21308/) (2025)
- [Beyond GFVC: A Progressive Face Video Compression Framework with Adaptive Visual Tokens](https://mlsanity.com/paper/2410.08485/) (2024)
- [Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation](https://mlsanity.com/paper/2401.08107/) (2024)
- [Standardizing Generative Face Video Compression using Supplemental Enhancement Information](https://mlsanity.com/paper/2410.15105/) (2024)
- [Pleno-Generation: A Scalable Generative Face Video Compression Framework with Bandwidth Intelligence](https://mlsanity.com/paper/2502.17085/) (2025)
- [Generative Models at the Frontier of Compression: A Survey on Generative Face Video Coding](https://mlsanity.com/paper/2506.07369/) (2025)
- [Compressing Human Body Video with Interactive Semantics: A Generative Approach](https://mlsanity.com/paper/2505.16152/) (2025)
- [Rethinking Generative Human Video Coding with Implicit Motion Transformation](https://mlsanity.com/paper/2506.10453/) (2025)
- [Sparse2Dense: A Keypoint-driven Generative Framework for Human Video Compression and Vertex Prediction](https://mlsanity.com/paper/2509.23169/) (2025)
- [FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution](https://mlsanity.com/paper/2506.11545/) (2025)

## Abstract (arXiv)

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.