The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Deep CNN-Based Blind Image Quality Predictor
Abstract
Image recognition based on convolutional neural networks (CNNs) has recently been shown to deliver the state-of-the-art performance in various areas of computer vision and image processing. Nevertheless, applying a deep CNN to no-reference image quality assessment (NR-IQA) remains a challenging task due to critical obstacles, i.e., the lack of a training database. In this paper, we propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method-deep image quality assessor (DIQA)-separates the training of NR-IQA into two stages: 1) an objective distortion part and 2) a human visual system-related part. In the first stage, the CNN learns to predict the objective error map, and then the model learns to predict subjective score in the second stage. To complement the inaccuracy of the objective error map prediction on the homogeneous region, we also propose a reliability map. Two simple handcrafted features were additionally employed to further enhance the accuracy. In addition, we propose a way to visualize perceptual error maps to analyze what was learned by the deep CNN model. In the experiments, the DIQA yielded the state-of-the-art accuracy on the various databases.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
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
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
Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features
Xue, W., Mou, X., Zhang, L., Bovik, A. C., & Feng, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2355716 · Cited by 641
Kim, J., Zeng, H., Ghadiyaram, D., Lee, S., Zhang, L., & Bovik, A. C. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2736018 · Cited by 283
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Cited by
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
Golestaneh, S. A., Dadsetan, S., & Kitani, K. M. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00404 · Cited by 465
Kim, J., Zeng, H., Ghadiyaram, D., Lee, S., Zhang, L., & Bovik, A. C. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2736018 · Cited by 283
Self-citationState-of-the-Art in 360° Video/Image Processing: Perception, Assessment and Compression
Xu, M., Li, C., Zhang, S., & Callet, P. L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2966864 · Cited by 281
Blind omnidirectional image quality assessment with viewport oriented graph convolutional networks
Xu, J., Zhou, W., & Chen, Z. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3015186 · Cited by 166
Metadata
- Authors
- Kim, J., Nguyen, A.-D., & Lee, S.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2019-01
- Volume
- 30
- Issue
- 1
- Pages
- 11-24
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2807793257
- Cited by
- 331
- References
- 7
- FWCI
- 17.82
- Type
- journal article
- Language
- en
- Open access
- closed
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