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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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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Robust Real-Time Face Detection
Viola, P., & Jones, M. J. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000013087.49260.fb · Cited by 14,097
Robust estimation of a location parameter
Huber, P. J. (1964) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177703732 · Cited by 7,228
Learning Transferable Architectures for Scalable Image Recognition
Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00907 · Cited by 6,875
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064
The Earth Mover's Distance as a Metric for Image Retrieval
Rubner, Y., Tomasi, C., & Guibas, L. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026543900054 · Cited by 5,547
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881959 · Cited by 3,018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp MinimaPreprint
Keskar, N. S., Mudigere, D., Nocedal, J. M., Smelyanskiy, M., & Tang, P. T. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.04836 · Cited by 2,270
Most apparent distortion: full-reference image quality assessment and the role of strategy
Larson, E. C., & Chandler, D. M. (2010) · Journal of Electronic Imaging · doi:10.1117/1.3267105 · Cited by 1,953
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
Moorthy, A. K., & Bovik, A. C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2147325 · Cited by 1,763
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
Saad, M. A., Bovik, A. C., & Charrier, C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2191563 · Cited by 1,750
Thomée, B., Shamma, D. A., Friedland, G. H., Elizalde, B., Ni, K. S., Poland, D., Borth, D., & Li, L.-J. (2016) · Communications of the ACM · doi:10.1145/2812802 · Cited by 1,715
Convolutional Neural Networks for No-Reference Image Quality Assessment
Kang, L., Ye, P., Li, Y., & Doermann, D. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.224 · Cited by 1,309
Image database TID2013: Peculiarities, results and perspectives
Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., et al. (2015) · Signal Processing: Image Communication · doi:10.1016/j.image.2014.10.009 · Cited by 1,241
A Two-Step Framework for Constructing Blind Image Quality Indices
Moorthy, A. K., & Bovik, A. C. (2010) · IEEE Signal Processing Letters · doi:10.1109/lsp.2010.2043888 · Cited by 1,196
Talebi, H., & Milanfar, P. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2831899 · Cited by 1,194
Understanding how image quality affects deep neural networks
Dodge, S., & Karam, L. (2016) · 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2016.7498955 · Cited by 835
Unsupervised feature learning framework for no-reference image quality assessment
Ye, P., Kumar, J., Kang, L., & Doermann, D. S. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247789 · Cited by 833
Image Signature: Highlighting Sparse Salient Regions
Hou, X., Harel, J., & Koch, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.146 · Cited by 832
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
Ghadiyaram, D., & Bovik, A. C. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2500021 · Cited by 782
Measuring colorfulness in natural images
Hasler, D., & Suesstrunk, S. E. (2003) · SPIE Proceedings · doi:10.1117/12.477378 · Cited by 729
No-reference image quality assessment based on spatial and spectral entropies
Liu, L., Liu, B., Huang, H., & Bovik, A. C. (2014) · Signal Processing Image Communication · doi:10.1016/j.image.2014.06.006 · Cited by 724
End-to-End Blind Image Quality Assessment Using Deep Neural Networks
Ma, K., Liu, W., Zhang, K., Duanmu, Z., Wang, Z., & Zuo, W. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2774045 · Cited by 613
RankIQA: Learning from Rankings for No-Reference Image Quality Assessment
Liu, X., van de Weijer, J., & Bagdanov, A. D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.118 · Cited by 560
KADID-10k: A Large-scale Artificially Distorted IQA Database
Lin, H., Hosu, V., & Saupe, D. (2019) · 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2019.8743252 · Cited by 523
Self-citationBlind Image Quality Assessment Based on High Order Statistics Aggregation
Xu, J., Ye, P., Li, Q., Du, H., Liu, Y., & Doermann, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2585880 · Cited by 490
On Advances in Statistical Modeling of Natural Images
Srivastava, A., Lee, A. B., Simoncelli, E. P., & Zhu, S. (2003) · Journal of Mathematical Imaging and Vision · doi:10.1023/a:1021889010444 · Cited by 488
On the use of deep learning for blind image quality assessment
Bianco, S., Celona, L., Napoletano, P., & Schettini, R. (2017) · Signal Image and Video Processing · doi:10.1007/s11760-017-1166-8 · Cited by 366
The Konstanz natural video database (KoNViD-1k)
Hosu, V., Hahn, F., Jenadeleh, M., Lin, H., Men, H., Sziranyi, T., Li, S., & Saupe, D. (2017) · 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2017.7965673 · Cited by 343
Self-citationRRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment
Soundararajan, R., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2166082 · Cited by 337
A Fast Wavelet-Based Algorithm for Global and Local Image Sharpness Estimation
Vu, P. V., & Chandler, D. M. (2012) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2199980 · Cited by 272
A deep neural network for image quality assessment
Bosse, S., Maniry, D., Wiegand, T., & Samek, W. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533065 · Cited by 230
CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms
Virtanen, T., Nuutinen, M., Vaahteranoksa, M., Oittinen, P. T., & Häkkinen, J. P. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2378061 · Cited by 177
Applications of Objective Image Quality Assessment Methods [Applications Corner]
Wang, Z. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.942295 · Cited by 167
MDID: A multiply distorted image database for image quality assessment
Sun, W. B., Zhou, F., & Liao, Q. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.033 · Cited by 120
Blind image quality prediction by exploiting multi-level deep representations
Gao, F., Yu, J., Zhu, S., Huang, Q., & Tian, Q. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.04.016 · Cited by 120
Group MAD Competition? A New Methodology to Compare Objective Image Quality Models
Ma, K., Wu, Q., Wang, Z., Duanmu, Z., Yong, H., Li, H., & Lei Zhang, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.184 · Cited by 94
No-reference image quality assessment with deep convolutional neural networks
Li, Y., Po, L.-M., Feng, L., & Fang, Y. (2016) · IEEE International Conference on Digital Signal Processing (DSP) · doi:10.1109/icdsp.2016.7868646 · Cited by 77
Deeprn: A Content Preserving Deep Architecture for Blind Image Quality Assessment
Varga, D., Saupe, D., & Szirányi, T. (2018) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2018.8486528 · Cited by 70
Self-citationLearning To Blindly Assess Image Quality In The Laboratory And Wild
Zhang, W., Ma, K., Zhai, G., & Yang, X. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191278 · Cited by 59
Blind image quality assessment on real distorted images using deep belief nets
Ghadiyaram, D., & Bovik, A. C. (2014) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2014.7032260 · Cited by 46
Dendi, S. V. R., Dev, C., Kothari, N., & Channappayya, S. S. (2018) · IEEE Signal Processing Letters · doi:10.1109/lsp.2018.2879518 · Cited by 46
A Reliable Methodology to Collect Ground Truth Data of Image Aesthetic Appeal
Siahaan, E., Hanjalić, A., & Redi, J. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2559942 · Cited by 34
MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation
Prabhushankar, M., Temel, D., & AlRegib, G. (2017) · Electronic Imaging · doi:10.2352/issn.2470-1173.2017.12.iqsp-223 · Cited by 26
Expertise screening in crowdsourcing image quality
Hosu, V., Lin, H., & Saupe, D. (2018) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2018.8463427 · Cited by 19
Self-citationArtifact reduction with diffusion preprocessing for image compression
Kopilovic, I., & Szirányi, T. (2005) · Optical Engineering · doi:10.1117/1.1849242 · Cited by 18
Shaping datasets: Optimal data selection for specific target distributions across dimensions
Vonikakis, V., Subramanian, R., & WINKLER, S. S. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533061 · Cited by 14
Learning to Detect Multiple Photographic Defects
Yu, N., Shen, X., Lin, Z., Měch, R., & Barnes, C. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00156 · Cited by 13
Siahaan, E., Hanjalić, A., & Redi, J. (2016) · Electronic Imaging · doi:10.2352/issn.2470-1173.2016.16.hvei-109 · Cited by 9
Visual Quality Assessment for Motion Compensated Frame Interpolation
Men, H., Lin, H., Hosu, V., Maurer, D., Bruhn, A., & Saupe, D. (2019) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2019.8743221 · Cited by 9
Self-citation
Research works printed but not linked: 5
- chollet, 2015, Keras
- gouvier, 2019, Ecological Validity
- van der maaten, 2008, Visualizing data using t-SNE, J Mach Learn Res, 9, 2579
- le callet, 2005, Subjective quality assessment IRCCyN/IVC database
- ponomarenko, 2009, TID2008—A database for evaluation of full-reference visual quality assessment metrics, Adv Modern Radioelectron, 10, 30
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Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network
Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., & Zhang, Y. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00372 · Cited by 963
Exploring CLIP for Assessing the Look and Feel of Images
Wang, J., Chan, K. C. K., & Loy, C. C. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25353 · Cited by 936
KADID-10k: A Large-scale Artificially Distorted IQA Database
Lin, H., Hosu, V., & Saupe, D. (2019) · 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2019.8743252 · Cited by 523
Self-citationMetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment
Zhu, H., Li, L., Wu, J., Dong, W., & Shi, G. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01415 · Cited by 467
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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