# Degraded Reference Image Quality Assessment

arXiv: https://arxiv.org/abs/2110.14899 · HTML: https://mlsanity.com/paper/2110.14899/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 72 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [Image Quality Assessment using Contrastive Learning](https://mlsanity.com/paper/2110.13266/) (2021)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://mlsanity.com/paper/2008.03889/) (2020)
- [Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment](https://mlsanity.com/paper/2108.07948/) (2021)
- [Predicting the Quality of Compressed Videos with Pre-Existing Distortions](https://mlsanity.com/paper/2004.02943/) (2020)
- [User-generated Video Quality Assessment: A Subjective and Objective Study](https://mlsanity.com/paper/2005.08527/) (2020)
- [Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge](https://mlsanity.com/paper/2109.12161/) (2021)

## Built on by (most cited first, 5 of 5)

- [Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment](https://mlsanity.com/paper/2312.04838/) (2023)
- [MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces](https://mlsanity.com/paper/2309.08113/) (2023)
- [Stochastic Surprisal: An inferential measurement of Free Energy in Neural Networks](https://mlsanity.com/paper/2302.05776/) (2023)
- [Image Quality Assessment: Enhancing Perceptual Exploration and Interpretation with Collaborative Feature Refinement and Hausdorff distance](https://mlsanity.com/paper/2412.15847/) (2024)
- [Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing](https://mlsanity.com/paper/2606.11577/) (2026)

## Abstract (arXiv)

In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable. On the other hand, intermediate references of degraded quality are often available, e.g., at the input of video transcoders, but how to make the best use of them in proper ways has not been deeply investigated. Here we make one of the first attempts to establish a new paradigm named degraded-reference IQA (DR IQA). Specifically, we lay out the architectures of DR IQA and introduce a 6-bit code to denote the choices of configurations. We construct the first large-scale databases dedicated to DR IQA and will make them publicly available. We make novel observations on distortion behavior in multi-stage distortion pipelines by comprehensively analyzing five multiple distortion combinations. Based on these observations, we develop novel DR IQA models and make extensive comparisons with a series of baseline models derived from top-performing FR and NR models. The results suggest that DR IQA may offer significant performance improvement in multiple distortion environments, thereby establishing DR IQA as a valid IQA paradigm that is worth further exploration.