# A strong baseline for image and video quality assessment

arXiv: https://arxiv.org/abs/2111.07104 · HTML: https://mlsanity.com/paper/2111.07104/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (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)
- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank](https://mlsanity.com/paper/1902.06285/) (2019)
- [Predictive No-Reference Assessment of Video Quality](https://mlsanity.com/paper/1604.07322/) (2016)

## Built on by (most cited first, 15 of 15)

- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model](https://mlsanity.com/paper/2407.21408/) (2024)
- [Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement](https://mlsanity.com/paper/2305.09512/) (2023)
- [Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance](https://mlsanity.com/paper/2405.03333/) (2024)
- [ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment](https://mlsanity.com/paper/2407.11496/) (2024)
- [VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results](https://mlsanity.com/paper/2508.18445/) (2025)
- [DIVA-VQA: Detecting Inter-frame Variations in UGC Video Quality](https://mlsanity.com/paper/2508.10605/) (2025)
- [CAMP-VQA: Caption-Embedded Multimodal Perception for No-Reference Quality Assessment of Compressed Video](https://mlsanity.com/paper/2511.07290/) (2025)
- [GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution](https://mlsanity.com/paper/2506.07897/) (2025)
- [MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training](https://mlsanity.com/paper/2508.07590/) (2025)
- [Exploring Simple Siamese Network for High-Resolution Video Quality Assessment](https://mlsanity.com/paper/2503.02330/) (2025)
- [DREAM: A Benchmark Study for Deepfake photoREalism AssessMent](https://mlsanity.com/paper/2510.10053/) (2025)

## Abstract (arXiv)

In this work, we present a simple yet effective unified model for perceptual quality assessment of image and video. In contrast to existing models which usually consist of complex network architecture, or rely on the concatenation of multiple branches of features, our model achieves a comparable performance by applying only one global feature derived from a backbone network (i.e. resnet18 in the presented work). Combined with some training tricks, the proposed model surpasses the current baselines of SOTA models on public and private datasets. Based on the architecture proposed, we release the models well trained for three common real-world scenarios: UGC videos in the wild, PGC videos with compression, Game videos with compression. These three pre-trained models can be directly applied for quality assessment, or be further fine-tuned for more customized usages. All the code, SDK, and the pre-trained weights of the proposed models are publicly available at https://github.com/Tencent/CenseoQoE.