# REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect

arXiv: https://arxiv.org/abs/2209.01760 · HTML: https://mlsanity.com/paper/2209.01760/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 98 references that are in the map.
- First posted on arXiv 5 Sep 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (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)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [PieAPP: Perceptual Image-Error Assessment through Pairwise Preference](https://mlsanity.com/paper/1806.02067/) (2018)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception](https://mlsanity.com/paper/2108.08505/) (2021)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning](https://mlsanity.com/paper/1804.01681/) (2018)
- [GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment](https://mlsanity.com/paper/2103.07666/) (2021)
- [Feedback Networks](https://mlsanity.com/paper/1612.09508/) (2016)
- [Blind Predicting Similar Quality Map for Image Quality Assessment](https://mlsanity.com/paper/1805.08493/) (2018)
- [Conceptual Compression via Deep Structure and Texture Synthesis](https://mlsanity.com/paper/2011.04976/) (2020)
- [RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment](https://mlsanity.com/paper/1712.05444/) (2017)
- [A Novel Just-Noticeable-Difference-based Saliency-Channel Attention Residual Network for Full-Reference Image Quality Predictions](https://mlsanity.com/paper/1902.05316/) (2019)

## Built on by (most cited first, 6 of 6)

- [Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models](https://mlsanity.com/paper/2408.02032/) (2024)
- [Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs](https://mlsanity.com/paper/2505.19678/) (2025)
- [Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wild](https://mlsanity.com/paper/2409.05540/) (2024)
- [Exploring Kolmogorov-Arnold networks for realistic image sharpness assessment](https://mlsanity.com/paper/2409.07762/) (2024)
- [Generalizable Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2410.03189/) (2024)
- [No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality Transfer](https://mlsanity.com/paper/2408.03885/) (2024)

## Abstract (arXiv)

Blind image quality assessment (BIQA) of user generated content (UGC) suffers from the range effect which indicates that on the overall quality range, mean opinion score (MOS) and predicted MOS (pMOS) are well correlated; focusing on a particular range, the correlation is lower. The reason for the range effect is that the predicted deviations both in a wide range and in a narrow range destroy the uniformity between MOS and pMOS. To tackle this problem, a novel method is proposed from coarse-grained metric to fine-grained prediction. Firstly, we design a rank-and-gradient loss for coarse-grained metric. The loss keeps the order and grad consistency between pMOS and MOS, thereby reducing the predicted deviation in a wide range. Secondly, we propose multi-level tolerance loss to make fine-grained prediction. The loss is constrained by a decreasing threshold to limite the predicted deviation in narrower and narrower ranges. Finally, we design a feedback network to conduct the coarse-to-fine assessment. On the one hand, the network adopts feedback blocks to process multi-scale distortion features iteratively and on the other hand, it fuses non-local context feature to the output of each iteration to acquire more quality-aware feature representation. Experimental results demonstrate that the proposed method can alleviate the range effect compared to the state-of-the-art methods effectively.