# KonIQ-10k: Towards an ecologically valid and large-scale IQA database

arXiv: https://arxiv.org/abs/1803.08489 · HTML: https://mlsanity.com/paper/1803.08489/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2018.

## Built on by (most cited first, 14 of 14)

- [RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated Content](https://mlsanity.com/paper/2101.10955/) (2021)
- [PyTorch Image Quality: Metrics for Image Quality Assessment](https://mlsanity.com/paper/2208.14818/) (2022)
- [MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion](https://mlsanity.com/paper/2205.10101/) (2022)
- [Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.15163/) (2024)
- [Deep Ranking with Adaptive Margin Triplet Loss](https://mlsanity.com/paper/2107.06187/) (2021)
- [ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images](https://mlsanity.com/paper/2407.21363/) (2024)
- [A survey on IQA](https://mlsanity.com/paper/2109.00347/) (2021)
- [BIQ2021: A Large-Scale Blind Image Quality Assessment Database](https://mlsanity.com/paper/2202.03879/) (2022)
- [UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics](https://mlsanity.com/paper/2204.08813/) (2022)
- [Telepresence Video Quality Assessment](https://mlsanity.com/paper/2207.09956/) (2022)
- [Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge](https://mlsanity.com/paper/2109.12161/) (2021)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [Helping Visually Impaired People Take Better Quality Pictures](https://mlsanity.com/paper/2305.08066/) (2023)
- [A Reference-Free Framework for Evaluating Single-Frame ISP Pipelines](https://mlsanity.com/paper/2607.23321/) (2026)

## Abstract (arXiv)

The main challenge in applying state-of-the-art deep learning methods to predict image quality in-the-wild is the relatively small size of existing quality scored datasets. The reason for the lack of larger datasets is the massive resources required in generating diverse and publishable content. We present a new systematic and scalable approach to create large-scale, authentic and diverse image datasets for Image Quality Assessment (IQA). We show how we built an IQA database, KonIQ-10k, consisting of 10,073 images, on which we performed very large scale crowdsourcing experiments in order to obtain reliable quality ratings from 1,467 crowd workers (1.2 million ratings). We argue for its ecological validity by analyzing the diversity of the dataset, by comparing it to state-of-the-art IQA databases, and by checking the reliability of our user studies.