# Regression or Classification? New Methods to Evaluate No-Reference Picture and Video Quality Models

arXiv: https://arxiv.org/abs/2102.00155 · HTML: https://mlsanity.com/paper/2102.00155/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 30 Jan 2021.

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

- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [Quality Assessment of In-the-Wild Videos](https://mlsanity.com/paper/1908.00375/) (2019)
- [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)
- [Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach](https://mlsanity.com/paper/1609.04757/) (2016)
- [Image Aesthetic Assessment: An Experimental Survey](https://mlsanity.com/paper/1610.00838/) (2016)
- [ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression](https://mlsanity.com/paper/1910.08845/) (2019)
- [A Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment](https://mlsanity.com/paper/2002.10651/) (2020)
- [Predicting the Quality of Compressed Videos with Pre-Existing Distortions](https://mlsanity.com/paper/2004.02943/) (2020)
- [BBAND Index: A No-Reference Banding Artifact Predictor](https://mlsanity.com/paper/2002.11891/) (2020)
- [Perceptual Video Quality Prediction Emphasizing Chroma Distortions](https://mlsanity.com/paper/2009.11203/) (2020)
- [No-Reference Video Quality Assessment Using Space-Time Chips](https://mlsanity.com/paper/2008.00031/) (2020)

## Built on by (most cited first, 4 of 4)

- [Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception](https://mlsanity.com/paper/2108.08505/) (2021)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [VMAF And Variants: Towards A Unified VQA](https://mlsanity.com/paper/2103.07770/) (2021)
- [Learning to Select Visual In-Context Demonstrations](https://mlsanity.com/paper/2603.26775/) (2026)

## Abstract (arXiv)

Video and image quality assessment has long been projected as a regression problem, which requires predicting a continuous quality score given an input stimulus. However, recent efforts have shown that accurate quality score regression on real-world user-generated content (UGC) is a very challenging task. To make the problem more tractable, we propose two new methods - binary, and ordinal classification - as alternatives to evaluate and compare no-reference quality models at coarser levels. Moreover, the proposed new tasks convey more practical meaning on perceptually optimized UGC transcoding, or for preprocessing on media processing platforms. We conduct a comprehensive benchmark experiment of popular no-reference quality models on recent in-the-wild picture and video quality datasets, providing reliable baselines for both evaluation methods to support further studies. We hope this work promotes coarse-grained perceptual modeling and its applications to efficient UGC processing.