# Modular Blind Video Quality Assessment

arXiv: https://arxiv.org/abs/2402.19276 · HTML: https://mlsanity.com/paper/2402.19276/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 51 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives](https://mlsanity.com/paper/2211.04894/) (2022)
- [FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling](https://mlsanity.com/paper/2207.02595/) (2022)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [Patch-VQ: 'Patching Up' the Video Quality Problem](https://mlsanity.com/paper/2011.13544/) (2020)
- [UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content](https://mlsanity.com/paper/2005.14354/) (2020)
- [SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition](https://mlsanity.com/paper/1904.04289/) (2019)
- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [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)
- [Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception](https://mlsanity.com/paper/2108.08505/) (2021)
- [Modular Deep Learning](https://mlsanity.com/paper/2302.11529/) (2023)
- [Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models](https://mlsanity.com/paper/2307.13981/) (2023)
- [Continual Learning for Blind Image Quality Assessment](https://mlsanity.com/paper/2102.09717/) (2021)
- [Subjective and Objective Quality Assessment of High Frame Rate Videos](https://mlsanity.com/paper/2007.11634/) (2020)
- [ChipQA: No-Reference Video Quality Prediction via Space-Time Chips](https://mlsanity.com/paper/2109.08726/) (2021)
- [A Subjective and Objective Study of Space-Time Subsampled Video Quality](https://mlsanity.com/paper/2102.00088/) (2021)
- [Deep Neural Network for Blind Visual Quality Assessment of 4K Content](https://mlsanity.com/paper/2206.04363/) (2022)
- [Assessment of Subjective and Objective Quality of Live Streaming Sports Videos](https://mlsanity.com/paper/2106.08431/) (2021)

## Built on by (most cited first, 23 of 23)

- [Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs](https://mlsanity.com/paper/2409.20063/) (2024)
- [Enhancing Blind Video Quality Assessment with Rich Quality-aware Features](https://mlsanity.com/paper/2405.08745/) (2024)
- [VQA2: Visual Question Answering for Video Quality Assessment](https://mlsanity.com/paper/2411.03795/) (2024)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [VidText: Towards Comprehensive Evaluation for Video Text Understanding](https://mlsanity.com/paper/2505.22810/) (2025)
- [VADB: A Large-Scale Video Aesthetic Database with Professional and Multi-Dimensional Annotations](https://mlsanity.com/paper/2510.25238/) (2025)
- [Scaling-up Perceptual Video Quality Assessment](https://mlsanity.com/paper/2505.22543/) (2025)
- [An Ensemble Approach to Short-form Video Quality Assessment Using Multimodal LLM](https://mlsanity.com/paper/2412.18060/) (2024)
- [Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models for Cross-Dataset Generalization](https://mlsanity.com/paper/2406.04654/) (2024)
- [ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos](https://mlsanity.com/paper/2412.20423/) (2024)
- [MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment](https://mlsanity.com/paper/2504.16003/) (2025)
- [Q-Router: Agentic Video Quality Assessment with Expert Model Routing and Artifact Localization](https://mlsanity.com/paper/2510.08789/) (2025)
- [A Multi-annotated and Multi-modal Dataset for Wide-angle Video Quality Assessment](https://mlsanity.com/paper/2501.12082/) (2025)
- [Context and Pixel Aware Large Language Model for Video Quality Assessment](https://mlsanity.com/paper/2505.16025/) (2025)
- [MDS-VQA: Model-Informed Data Selection for Video Quality Assessment](https://mlsanity.com/paper/2603.11525/) (2026)
- [rPPG-VQA: A Video Quality Assessment Framework for Unsupervised rPPG Training](https://mlsanity.com/paper/2604.11156/) (2026)
- [Subjective and Objective Quality Assessment of Banding Artifacts on Compressed Videos](https://mlsanity.com/paper/2508.08700/) (2025)
- [No-Reference Rendered Video Quality Assessment: Dataset and Metrics](https://mlsanity.com/paper/2510.13349/) (2025)
- [VQ-Jarvis: Retrieval-Augmented Video Restoration Agent with Sharp Vision and Fast Thought](https://mlsanity.com/paper/2603.22998/) (2026)
- [LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models](https://mlsanity.com/paper/2607.01086/) (2026)
- [Contrastive Order Learning: A General Framework for Ordinal Regression](https://mlsanity.com/paper/2607.08109/) (2026)
- [High-Fidelity Video Quality Assessment with VQA-Specific Saliency](https://mlsanity.com/paper/2609.16946/) (2026)
- [LLaVA-Assessor: Building the Foundation LMM For Visual Quality Assessment](https://mlsanity.com/paper/2609.26205/) (2026)

## Abstract (arXiv)

Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services. Contemporary deep learning-based models primarily analyze video content in its aggressively subsampled format, while being blind to the impact of the actual spatial resolution and frame rate on video quality. In this paper, we propose a modular BVQA model and a method of training it to improve its modularity. Our model comprises a base quality predictor, a spatial rectifier, and a temporal rectifier, responding to the visual content and distortion, spatial resolution, and frame rate changes on video quality, respectively. During training, spatial and temporal rectifiers are dropped out with some probabilities to render the base quality predictor a standalone BVQA model, which should work better with the rectifiers. Extensive experiments on both professionally-generated content and user-generated content video databases show that our quality model achieves superior or comparable performance to current methods. Additionally, the modularity of our model offers an opportunity to analyze existing video quality databases in terms of their spatial and temporal complexity.