Hierarchical Curriculum Learning for No-Reference Image Quality Assessment
Author:
Funder
Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s11263-023-01851-5.pdf
Reference72 articles.
1. A, T. D., B, K. G., C, L. N., et al. (2018). Referenceless quality metric of multiply-distorted images based on structural degradation. Neurocomputing, 290, 185–195.
2. Bosse, S., Maniry, D., Müller, K. R., et al. (2017). Deep neural networks for no-reference and full-reference image quality assessment. IEEE Transactions on Image Processing (TIP), 27(1), 206–219.
3. Ciancio, A., da Silva, E. A., Said, A., et al. (2010). No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Transactions on Image Processing (TIP), 20(1), 64–75.
4. Deng, J., Dong, W., Socher, R., et al. (2009). Imagenet: A large-scale hierarchical image database. In Conference on computer vision and pattern recognition (CVPR) (pp. 248–255).
5. Fang, Y., Ma, K., Wang, Z., et al. (2015). No-reference quality assessment of contrast-distorted images based on natural scene statistics. IEEE Signal Processing Letters, 22(7), 838–842. https://doi.org/10.1109/LSP.2014.2372333
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