# A comprehensive evaluation of full-reference image quality assessment algorithms on KADID-10k

arXiv: https://arxiv.org/abs/1907.02096 · HTML: https://mlsanity.com/paper/1907.02096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2019.

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

- [JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference](https://mlsanity.com/paper/2512.22999/) (2025)

## Abstract (arXiv)

Significant progress has been made in the past decade for full-reference image quality assessment (FR-IQA). However, new large scale image quality databases have been released for evaluating image quality assessment algorithms. In this study, our goal is to give a comprehensive evaluation of state-of-the-art FR-IQA metrics using the recently published KADID-10k database which is largest available one at the moment. Our evaluation results and the associated discussions is very helpful to obtain a clear understanding about the status of state-of-the-art FR-IQA metrics.