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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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Quality assessment of higher resolution images and videos with remote testing
Göring, S., Rao, R. R. R., & Raake, A. (2023) · Quality and User Experience · doi:10.1007/s41233-023-00055-6 · Cited by 17
BIQ2021: a large-scale blind image quality assessment database
Ahmed, N., & Asif, S. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.5.053010 · Cited by 17
Song, M., Chen, C., Song, W., & Fang, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3541588 · Cited by 18
Omnidirectional Video Quality Assessment With Causal Intervention
Hu, Z., Liu, L., & Sang, Q. (2024) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2023.3342707 · Cited by 16
Perceptual Quality Assessment of Virtual Reality Videos in the Wild
Wen, W., Li, M., Yao, Y., Sui, X., Zhang, Y., Lan, L., Fang, Y., & Ma, K. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3378352 · Cited by 17
Blind Image Quality Assessment Based on Perceptual Comparison
Li, A., Wu, J., Liu, Y., Li, L., Dong, W., & Shi, G. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3397051 · Cited by 16
Quality-aware blind image motion deblurring
Song, T., Li, L., Wu, J., Dong, W., & Cheng, D. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110568 · Cited by 16
Brain-Inspired Image Perceptual Quality Assessment Based on EEG: A QoE Perspective
Hu, S., Duan, Y., Tao, X., Li, G. Y., Lü, J., Liu, G., Zheng, Z., & Pan, C. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3408684 · Cited by 17
No-Reference Image Quality Assessment: Obtain MOS From Image Quality Score Distribution
Gao, Y., Min, X., Cao, Y., Liu, X., & Zhai, G. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3485684 · Cited by 22
Disregarding the Big Picture: Towards Local Image Quality Assessment
Wiedemann, O., Hosu, V., Lin, H., & Saupe, D. (2018) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2018.8463384 · Cited by 16
Self-citationNo-Reference Quality Assessment of In-Capture Distorted Videos
Agarla, M., Celona, L., & Schettini, R. (2020) · Journal of Imaging · doi:10.3390/jimaging6080074 · Cited by 16
Regression or classification? New methods to evaluate no-reference picture and video quality models
Tu, Z., Chen, C.-J., Chen, L., Wang, Y., Birkbeck, N., Adsumilli, B., & Bovik, A. C. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414232 · Cited by 16
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
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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.
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