# Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach

arXiv: https://arxiv.org/abs/2305.12726 · HTML: https://mlsanity.com/paper/2305.12726/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 77 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 30 of 47)

- [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)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Expanding Language-Image Pretrained Models for General Video Recognition](https://mlsanity.com/paper/2208.02816/) (2022)
- [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://mlsanity.com/paper/2012.02181/) (2020)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2011.06294/) (2020)
- [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)
- [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)
- [A Deep Learning based No-reference Quality Assessment Model for UGC Videos](https://mlsanity.com/paper/2204.14047/) (2022)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [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)

## Built on by (most cited first, 30 of 45)

- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [KVQ: Kwai Video Quality Assessment for Short-form Videos](https://mlsanity.com/paper/2402.07220/) (2024)
- [NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results](https://mlsanity.com/paper/2404.11313/) (2024)
- [FineVQ: Fine-Grained User Generated Content Video Quality Assessment](https://mlsanity.com/paper/2412.19238/) (2024)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [Exploring the Naturalness of AI-Generated Images](https://mlsanity.com/paper/2312.05476/) (2023)
- [Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models](https://mlsanity.com/paper/2312.15300/) (2023)
- [NTIRE 2024 Quality Assessment of AI-Generated Content Challenge](https://mlsanity.com/paper/2404.16687/) (2024)
- [Enhancing Blind Video Quality Assessment with Rich Quality-aware Features](https://mlsanity.com/paper/2405.08745/) (2024)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings](https://mlsanity.com/paper/2311.07090/) (2023)
- [VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learning](https://mlsanity.com/paper/2508.06051/) (2025)
- [Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated Videos](https://mlsanity.com/paper/2312.15425/) (2023)
- [PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition](https://mlsanity.com/paper/2404.13299/) (2024)
- [NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results](https://mlsanity.com/paper/2506.02875/) (2025)
- [Scaling-up Perceptual Video Quality Assessment](https://mlsanity.com/paper/2505.22543/) (2025)
- [BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos](https://mlsanity.com/paper/2506.20103/) (2025)
- [BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos](https://mlsanity.com/paper/2312.08859/) (2023)
- [Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models for Cross-Dataset Generalization](https://mlsanity.com/paper/2406.04654/) (2024)
- [Explore the Hallucination on Low-level Perception for MLLMs](https://mlsanity.com/paper/2409.09748/) (2024)
- [Enabling Versatile Controls for Video Diffusion Models](https://mlsanity.com/paper/2503.16983/) (2025)
- [MVAD: A Multiple Visual Artifact Detector for Video Streaming](https://mlsanity.com/paper/2406.00212/) (2024)
- [Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search](https://mlsanity.com/paper/2502.05924/) (2025)
- [MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment](https://mlsanity.com/paper/2504.16003/) (2025)
- [MDIQA: Unified Image Quality Assessment for Multi-dimensional Evaluation and Restoration](https://mlsanity.com/paper/2508.16887/) (2025)
- [Q-Router: Agentic Video Quality Assessment with Expert Model Routing and Artifact Localization](https://mlsanity.com/paper/2510.08789/) (2025)
- [CAMP-VQA: Caption-Embedded Multimodal Perception for No-Reference Quality Assessment of Compressed Video](https://mlsanity.com/paper/2511.07290/) (2025)
- [Secure Video Quality Assessment Resisting Adversarial Attacks](https://mlsanity.com/paper/2410.06866/) (2024)
- [Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model and Training Strategy](https://mlsanity.com/paper/2503.20673/) (2025)
- [DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment](https://mlsanity.com/paper/2504.11733/) (2025)

## Abstract (arXiv)

The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment (VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQA on in-the-wild videos is especially challenging as it could be affected by complicated factors, including various distortions and diverse contents. Though subjective studies have collected overall quality scores for these videos, how the abstract quality scores relate with specific factors is still obscure, hindering VQA methods from more concrete quality evaluations (e.g. sharpness of a video). To solve this problem, we collect over two million opinions on 4,543 in-the-wild videos on 13 dimensions of quality-related factors, including in-capture authentic distortions (e.g. motion blur, noise, flicker), errors introduced by compression and transmission, and higher-level experiences on semantic contents and aesthetic issues (e.g. composition, camera trajectory), to establish the multi-dimensional Maxwell database. Specifically, we ask the subjects to label among a positive, a negative, and a neutral choice for each dimension. These explanation-level opinions allow us to measure the relationships between specific quality factors and abstract subjective quality ratings, and to benchmark different categories of VQA algorithms on each dimension, so as to more comprehensively analyze their strengths and weaknesses. Furthermore, we propose the MaxVQA, a language-prompted VQA approach that modifies vision-language foundation model CLIP to better capture important quality issues as observed in our analyses. The MaxVQA can jointly evaluate various specific quality factors and final quality scores with state-of-the-art accuracy on all dimensions, and superb generalization ability on existing datasets. Code and data available at https://github.com/VQAssessment/MaxVQA.