# BIQ2021: A Large-Scale Blind Image Quality Assessment Database

arXiv: https://arxiv.org/abs/2202.03879 · HTML: https://mlsanity.com/paper/2202.03879/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [KonIQ-10k: Towards an ecologically valid and large-scale IQA database](https://mlsanity.com/paper/1803.08489/) (2018)

## Built on by (most cited first, 6 of 6)

- [Enhanced Dermatology Image Quality Assessment via Cross-Domain Training](https://mlsanity.com/paper/2506.16116/) (2025)
- [SR-Ground: Image Quality Grounding for Super-Resolved Content](https://mlsanity.com/paper/2605.21244/) (2026)
- [MetaQAP - A Meta-Learning Approach for Quality-Aware Pretraining in Image Quality Assessment](https://mlsanity.com/paper/2506.16601/) (2025)
- [A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement](https://mlsanity.com/paper/2506.18323/) (2025)
- [A Lightweight Global-Target Framework for Multi-Domain No-Reference Image Quality Assessment in UAV Imagery](https://mlsanity.com/paper/2604.13112/) (2026)
- [MCIQA-2K: A Multi-Dimensional Dataset and No-Reference Quality Assessment Benchmark for Colorized Images](https://mlsanity.com/paper/2609.14495/) (2026)

## Abstract (arXiv)

The assessment of the perceptual quality of digital images is becoming increasingly important as a result of the widespread use of digital multimedia devices. Smartphones and high-speed internet are just two examples of technologies that have multiplied the amount of multimedia content available. Thus, obtaining a representative dataset, which is required for objective quality assessment training, is a significant challenge. The Blind Image Quality Assessment Database, BIQ2021, is presented in this article. By selecting images with naturally occurring distortions and reliable labeling, the dataset addresses the challenge of obtaining representative images for no-reference image quality assessment. The dataset consists of three sets of images: those taken without the intention of using them for image quality assessment, those taken with intentionally introduced natural distortions, and those taken from an open-source image-sharing platform. It is attempted to maintain a diverse collection of images from various devices, containing a variety of different types of objects and varying degrees of foreground and background information. To obtain reliable scores, these images are subjectively scored in a laboratory environment using a single stimulus method. The database contains information about subjective scoring, human subject statistics, and the standard deviation of each image. The dataset's Mean Opinion Scores (MOS) make it useful for assessing visual quality. Additionally, the proposed database is used to evaluate existing blind image quality assessment approaches, and the scores are analyzed using Pearson and Spearman's correlation coefficients. The image database and MOS are freely available for use and benchmarking.