# A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment

arXiv: https://arxiv.org/abs/1705.05126 · HTML: https://mlsanity.com/paper/1705.05126/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2017.

## Built on by (most cited first, 13 of 13)

- [Domain Adaptation for Underwater Image Enhancement](https://mlsanity.com/paper/2108.09650/) (2021)
- [Confusing Image Quality Assessment: Towards Better Augmented Reality Experience](https://mlsanity.com/paper/2204.04900/) (2022)
- [No-Reference Image Quality Assessment by Hallucinating Pristine Features](https://mlsanity.com/paper/2108.04165/) (2021)
- [GS-QA: Comprehensive Quality Assessment Benchmark for Gaussian Splatting View Synthesis](https://mlsanity.com/paper/2502.13196/) (2025)
- [Probabilistic Embeddings Revisited](https://mlsanity.com/paper/2202.06768/) (2022)
- [Deep Ensembling for Perceptual Image Quality Assessment](https://mlsanity.com/paper/2305.09141/) (2023)
- [Blind VQA on 360deg Video via Progressively Learning from Pixels, Frames and Video](https://mlsanity.com/paper/2111.09503/) (2021)
- [Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment](https://mlsanity.com/paper/2209.07126/) (2022)
- [From Global to Granular: Revealing IQA Model Performance via Correlation Surface](https://mlsanity.com/paper/2601.21738/) (2026)
- [A review on Machine Learning based User-Centric Multimedia Streaming Techniques](https://mlsanity.com/paper/2411.15801/) (2024)
- [Related Work on Image Quality Assessment](https://mlsanity.com/paper/2111.06291/) (2021)
- [A Review of Deep Learning Based Image Super-resolution Techniques](https://mlsanity.com/paper/2201.10521/) (2022)
- [Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration](https://mlsanity.com/paper/2311.15846/) (2023)

## Abstract (arXiv)

In the field of objective image quality assessment (IQA), the Spearman's ρ and Kendall's τ are two most popular rank correlation indicators, which straightforwardly assign uniform weight to all quality levels and assume each pair of images are sortable. They are successful for measuring the average accuracy of an IQA metric in ranking multiple processed images. However, two important perceptual properties are ignored by them as well. Firstly, the sorting accuracy (SA) of high quality images are usually more important than the poor quality ones in many real world applications, where only the top-ranked images would be pushed to the users. Secondly, due to the subjective uncertainty in making judgement, two perceptually similar images are usually hardly sortable, whose ranks do not contribute to the evaluation of an IQA metric. To more accurately compare different IQA algorithms, we explore a perceptually weighted rank correlation indicator in this paper, which rewards the capability of correctly ranking high quality images, and suppresses the attention towards insensitive rank mistakes. More specifically, we focus on activating `valid' pairwise comparison towards image quality, whose difference exceeds a given sensory threshold (ST). Meanwhile, each image pair is assigned an unique weight, which is determined by both the quality level and rank deviation. By modifying the perception threshold, we can illustrate the sorting accuracy with a more sophisticated SA-ST curve, rather than a single rank correlation coefficient. The proposed indicator offers a new insight for interpreting visual perception behaviors. Furthermore, the applicability of our indicator is validated in recommending robust IQA metrics for both the degraded and enhanced image data.