# Predicting the Quality of Compressed Videos with Pre-Existing Distortions

arXiv: https://arxiv.org/abs/2004.02943 · HTML: https://mlsanity.com/paper/2004.02943/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 21 of 21)

- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [MD-VQA: Multi-Dimensional Quality Assessment for UGC Live Videos](https://mlsanity.com/paper/2303.14933/) (2023)
- [Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models](https://mlsanity.com/paper/2307.13981/) (2023)
- [FineVQ: Fine-Grained User Generated Content Video Quality Assessment](https://mlsanity.com/paper/2412.19238/) (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)
- [Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement](https://mlsanity.com/paper/2305.09512/) (2023)
- [CLIPVQA:Video Quality Assessment via CLIP](https://mlsanity.com/paper/2407.04928/) (2024)
- [FAVER: Blind Quality Prediction of Variable Frame Rate Videos](https://mlsanity.com/paper/2201.01492/) (2022)
- [Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance](https://mlsanity.com/paper/2405.03333/) (2024)
- [Degraded Reference Image Quality Assessment](https://mlsanity.com/paper/2110.14899/) (2021)
- [Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models](https://mlsanity.com/paper/2102.00155/) (2021)
- [Perceptual Quality Assessment of UGC Gaming Videos](https://mlsanity.com/paper/2204.00128/) (2022)
- [FOVQA: Blind Foveated Video Quality Assessment](https://mlsanity.com/paper/2106.13328/) (2021)
- [Deep Quality Assessment of Compressed Videos: A Subjective and Objective Study](https://mlsanity.com/paper/2205.03630/) (2022)
- [BVI-UGC: A Video Quality Database for User-Generated Content Transcoding](https://mlsanity.com/paper/2408.07171/) (2024)
- [Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming](https://mlsanity.com/paper/2409.17596/) (2024)
- [LEHA-CVQAD: Dataset To Enable Generalized Video Quality Assessment of Compression Artifacts](https://mlsanity.com/paper/2507.03990/) (2025)
- [VideoAesBench: Benchmarking the Video Aesthetics Perception Capabilities of Large Multimodal Models](https://mlsanity.com/paper/2601.21915/) (2026)
- [Analytic Score Optimization for Multi Dimension Video Quality Assessment](https://mlsanity.com/paper/2602.16856/) (2026)
- [A Mamba-based Perceptual Loss Function for Learning-based UGC Transcoding](https://mlsanity.com/paper/2603.25566/) (2026)

## Abstract (arXiv)

Over the past decade, the online video industry has greatly expanded the volume of visual data that is streamed and shared over the Internet. Moreover, because of the increasing ease of video capture, many millions of consumers create and upload large volumes of User-Generated-Content (UGC) videos. Unlike streaming television or cinematic content produced by professional videographers and cinemagraphers, UGC videos are most commonly captured by naive users having limited skills and imperfect technique, and often are afflicted by highly diverse and mixed in-capture distortions. These UGC videos are then often uploaded for sharing onto cloud servers, where they further compressed for storage and transmission. Our paper tackles the highly practical problem of predicting the quality of compressed videos (perhaps during the process of compression, to help guide it), with only (possibly severely) distorted UGC videos as references. To address this problem, we have developed a novel Video Quality Assessment (VQA) framework that we call 1stepVQA (to distinguish it from two-step methods that we discuss). 1stepVQA overcomes limitations of Full-Reference, Reduced-Reference and No-Reference VQA models by exploiting the statistical regularities of both natural videos and distorted videos. We show that 1stepVQA is able to more accurately predict the quality of compressed videos, given imperfect reference videos. We also describe a new dedicated video database which includes (typically distorted) UGC reference videos, and a large number of compressed versions of them. We show that the 1stepVQA model outperforms other VQA models in this scenario. We are providing the dedicated new database free of charge at https://live.ece.utexas.edu/research/onestep/index.html