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KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment
Open accessAbstract
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512 × 384). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.
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“The most probable reason would be that these models were trained on picture quality datasets [64], [70], which contain different types of (strictly spatial) distortions than UGC-VQA databases.”
From UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2021) · cites this paper as [70]
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Reqa: Coarse-to-Fine Assessment of Image Quality to Alleviate the Range EffectPreprint
Li, B., & Huo, F. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4594552 · Cited by 3
Interval-Based Least Squares for Uncertainty-Aware Learning in Human-Centric Multimedia Systems
Narwaria, M., & Tatu, A. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3025834 · Cited by 2
Deng, G. (2024) · Applied Mathematical Modelling · doi:10.1016/j.apm.2024.01.036 · Cited by 2
Blind image quality assessment with semi-supervised learning
Li, X., Wang, Z., & Xu, B. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104100 · Cited by 2
Aesthetic Perception Prompting for Interpretable Image Aesthetics Assessment with MLLMs
Wang, L., Qiao, Z., Chen, R., Li, J., Wang, W., Wang, X., Rao, W., Chen, S., & Liu, A. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889477 · Cited by 2
Explore the Hallucination on Low-level Perception for MLLMs
Sun, Y., Zhang, Z., Wu, H., Liu, X., Lin, W., Zhai, G., & Min, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888437 · Cited by 5
Stochastic BIQA: Median randomized smoothing for certified blind image quality assessment
Shumitskaya, E., Pautov, M., Vatolin, D. S., & Antsiferova, A. (2025) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2025.104447 · Cited by 2
Triqa: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets
Sureddi, R., Zadtootaghaj, S., Barman, N., & Bovik, A. C. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084443 · Cited by 2
Holistic Coreset Selection for Data Efficient Image Quality Assessment
Nema, A., Zhu, H., & Lin, W. (2025) · Proceedings - International Conference on Image Processing · doi:10.1109/icip55913.2025.11084586 · Cited by 2
Metadata
- Authors
- Hosu, V., Lin, H., Sziranyi, T., & Saupe, D.
- Affiliations
- University of Konstanz (DE)
- HUN-REN Institute for Computer Science and Control (HU)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2020
- Volume
- 29
- Pages
- 4041-4056
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W3002992380
- Cited by
- 759
- References
- 59
- Citations by year
- 2026: 116 · 2025: 165 · 2024: 147 · 2023: 127 · 2022: 91 · 2021: 73 · 2020: 22
- FWCI
- 30.42
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- KOPS (University of Konstanz) (submittedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft (251654672 – TRR 161 (Project A05))
- Hungarian Scientific Research Fund (OTKA 120499)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- no-reference image quality assessment, image quality database, ecological validity, in-the-wild images, high-resolution image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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