# Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality Assessment

arXiv: https://arxiv.org/abs/2409.07115 · HTML: https://mlsanity.com/paper/2409.07115/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [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)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Harmonic Networks: Deep Translation and Rotation Equivariance](https://mlsanity.com/paper/1612.04642/) (2016)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)
- [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)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [Rethinking Vision Transformers for MobileNet Size and Speed](https://mlsanity.com/paper/2212.08059/) (2022)
- [Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild](https://mlsanity.com/paper/2304.00451/) (2023)
- [Transformer for Image Quality Assessment](https://mlsanity.com/paper/2101.01097/) (2020)
- [A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction](https://mlsanity.com/paper/1708.08190/) (2017)
- [Learning to Rank for Blind Image Quality Assessment](https://mlsanity.com/paper/1309.0213/) (2013)
- [Test Time Adaptation for Blind Image Quality Assessment](https://mlsanity.com/paper/2307.14735/) (2023)
- [Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency](https://mlsanity.com/paper/2206.04318/) (2022)

## Built on by (most cited first, 1 of 1)

- [Describe-to-Score: A text-guided framework for image complexity assessment](https://mlsanity.com/paper/2509.16609/) (2025)

## Abstract (arXiv)

The no-reference image quality assessment is a challenging domain that addresses estimating image quality without the original reference. We introduce an improved mechanism to extract local and non-local information from images via different transformer encoders and CNNs. The utilization of Transformer encoders aims to mitigate locality bias and generate a non-local representation by sequentially processing CNN features, which inherently capture local visual structures. Establishing a stronger connection between subjective and objective assessments is achieved through sorting within batches of images based on relative distance information. A self-consistency approach to self-supervision is presented, explicitly addressing the degradation of no-reference image quality assessment (NR-IQA) models under equivariant transformations. Our approach ensures model robustness by maintaining consistency between an image and its horizontally flipped equivalent. Through empirical evaluation of five popular image quality assessment datasets, the proposed model outperforms alternative algorithms in the context of no-reference image quality assessment datasets, especially on smaller datasets. Codes are available at hrefhttps://github.com/mas94/ADTRShttps://github.com/mas94/ADTRS