# Large-Scale Study of Perceptual Video Quality

arXiv: https://arxiv.org/abs/1803.01761 · HTML: https://mlsanity.com/paper/1803.01761/

## Key facts

- Cited by 127 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2018.

## Built on by (most cited first, 30 of 127)

- [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)
- [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)
- [On the Content Bias in Fr'echet Video Distance](https://mlsanity.com/paper/2404.12391/) (2024)
- [Towards A Better Metric for Text-to-Video Generation](https://mlsanity.com/paper/2401.07781/) (2024)
- [Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment](https://mlsanity.com/paper/2210.05357/) (2022)
- [MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos](https://mlsanity.com/paper/2303.14933/) (2023)
- [Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach](https://mlsanity.com/paper/2305.12726/) (2023)
- [DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment](https://mlsanity.com/paper/2206.09853/) (2022)
- [Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models](https://mlsanity.com/paper/2307.13981/) (2023)
- [KVQ: Kwai Video Quality Assessment for Short-form Videos](https://mlsanity.com/paper/2402.07220/) (2024)
- [LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models](https://mlsanity.com/paper/2408.14008/) (2024)
- [FineVQ: Fine-Grained User Generated Content Video Quality Assessment](https://mlsanity.com/paper/2412.19238/) (2024)
- [Deep Learning based Full-reference and No-reference Quality Assessment Models for Compressed UGC Videos](https://mlsanity.com/paper/2106.01111/) (2021)
- [Video compression dataset and benchmark of learning-based video-quality metrics](https://mlsanity.com/paper/2211.12109/) (2022)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [VQ-Insight: Teaching VLMs for AI-Generated Video Quality Understanding via Progressive Visual Reinforcement Learning](https://mlsanity.com/paper/2506.18564/) (2025)
- [Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment](https://mlsanity.com/paper/2207.03723/) (2022)
- [Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion](https://mlsanity.com/paper/2302.13269/) (2023)
- [Modular Blind Video Quality Assessment](https://mlsanity.com/paper/2402.19276/) (2024)
- [ChipQA: No-Reference Video Quality Prediction via Space-Time Chips](https://mlsanity.com/paper/2109.08726/) (2021)
- [Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models](https://mlsanity.com/paper/2312.15300/) (2023)
- [Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment](https://mlsanity.com/paper/2304.06440/) (2023)
- [Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment](https://mlsanity.com/paper/2308.00729/) (2023)
- [CONVIQT: Contrastive Video Quality Estimator](https://mlsanity.com/paper/2206.14713/) (2022)
- [Enhancing Blind Video Quality Assessment with Rich Quality-aware Features](https://mlsanity.com/paper/2405.08745/) (2024)
- [Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment](https://mlsanity.com/paper/2307.16813/) (2023)
- [PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild](https://mlsanity.com/paper/2405.17765/) (2024)
- [Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment](https://mlsanity.com/paper/2304.14672/) (2023)

## Abstract (arXiv)

The great variations of videographic skills, camera designs, compression and processing protocols, and displays lead to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions. As such, these databases fail to adequately represent real world videos, which contain very different kinds of content obtained under highly diverse imaging conditions and are subject to authentic, often commingled distortions that are impossible to simulate. As a result, NR video quality predictors tested on real-world video data often perform poorly. Towards advancing NR video quality prediction, we constructed a large-scale video quality assessment database containing 585 videos of unique content, captured by a large number of users, with wide ranges of levels of complex, authentic distortions. We collected a large number of subjective video quality scores via crowdsourcing. A total of 4776 unique participants took part in the study, yielding more than 205000 opinion scores, resulting in an average of 240 recorded human opinions per video. We demonstrate the value of the new resource, which we call the LIVE Video Quality Challenge Database (LIVE-VQC), by conducting a comparison of leading NR video quality predictors on it. This study is the largest video quality assessment study ever conducted along several key dimensions: number of unique contents, capture devices, distortion types and combinations of distortions, study participants, and recorded subjective scores. The database is available for download on this link: http://live.ece.utexas.edu/research/LIVEVQC/index.html .