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 for PaQ-2-PiQ and KonCept512's poor performance is that they were trained on picture quality datasets which contain different types of (strictly spatial) distortions than UGC-VQA databases.
“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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- 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 · 2019: 12 · 2018: 6
- 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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