# Learning to Rank for Blind Image Quality Assessment

arXiv: https://arxiv.org/abs/1309.0213 · HTML: https://mlsanity.com/paper/1309.0213/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2013.

## Built on by (most cited first, 25 of 25)

- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank](https://mlsanity.com/paper/2505.14460/) (2025)
- [Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare](https://mlsanity.com/paper/2405.19298/) (2024)
- [Quantifying Visual Image Quality: A Bayesian View](https://mlsanity.com/paper/2102.00195/) (2021)
- [Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap](https://mlsanity.com/paper/2404.13573/) (2024)
- [SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning](https://mlsanity.com/paper/2411.10161/) (2024)
- [VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment](https://mlsanity.com/paper/2408.11481/) (2024)
- [On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement](https://mlsanity.com/paper/2307.12027/) (2023)
- [Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric](https://mlsanity.com/paper/2208.00623/) (2022)
- [MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction](https://mlsanity.com/paper/2509.09082/) (2025)
- [Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches](https://mlsanity.com/paper/2207.14769/) (2022)
- [GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment](https://mlsanity.com/paper/2401.10511/) (2024)
- [Learning semantic image quality for fetal ultrasound from noisy ranking annotation](https://mlsanity.com/paper/2402.08294/) (2024)
- [Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment](https://mlsanity.com/paper/2106.06784/) (2021)
- [TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering](https://mlsanity.com/paper/2509.01312/) (2025)
- [Learning Transformer Features for Image Quality Assessment](https://mlsanity.com/paper/2112.00485/) (2021)
- [Pairwise Comparisons Are All You Need](https://mlsanity.com/paper/2403.09746/) (2024)
- [MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation](https://mlsanity.com/paper/2409.01212/) (2024)
- [Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality Assessment](https://mlsanity.com/paper/2409.07115/) (2024)
- [No-reference geometry quality assessment for colorless point clouds via list-wise rank learning](https://mlsanity.com/paper/2502.11726/) (2025)
- [Image Quality Assessment: Learning to Rank Image Distortion Level](https://mlsanity.com/paper/2208.03317/) (2022)
- [Annotation-Free Human Sketch Quality Assessment](https://mlsanity.com/paper/2507.20548/) (2025)
- [ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking](https://mlsanity.com/paper/2603.20785/) (2026)
- [MR-IQA: A Unified Margin View of Regression and Ranking for Blind Image Quality Assessment](https://mlsanity.com/paper/2606.29760/) (2026)
- [Di2CycleSB: Towards High-Quality Unsupervised Nighttime Visibility Enhancement via Schr"odinger Bridge Transformer](https://mlsanity.com/paper/2608.29043/) (2026)

## Abstract (arXiv)

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as "the quality of image Ia is better than that of image Ib" for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves comparable performance to state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.