The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Fully Deep Blind Image Quality Predictor
Abstract
In general, owing to the benefits obtained from original information, full-reference image quality assessment (FR-IQA) achieves relatively higher prediction accuracy than no-reference image quality assessment (NR-IQA). By fully utilizing reference images, conventional FR-IQA methods have been investigated to produce objective scores that are close to subjective scores. In contrast, NR-IQA does not consider reference images; thus, its performance is inferior to that of FR-IQA. To alleviate this accuracy discrepancy between FR-IQA and NR-IQA methods, we propose a blind image evaluator based on a convolutional neural network (BIECON). To imitate FR-IQA behavior, we adopt the strong representation power of a deep convolutional neural network to generate a local quality map, similar to FR-IQA. To obtain the best results from the deep neural network, replacing hand-crafted features with automatically learned features is necessary. To apply the deep model to the NR-IQA framework, three critical problems must be resolved: 1) lack of training data; 2) absence of local ground truth targets; and 3) different purposes of feature learning. BIECON follows the FR-IQA behavior using the local quality maps as intermediate targets for conventional neural networks, which leads to NR-IQA prediction accuracy that is comparable with that of state-of-the-art FR-IQA methods.
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Cites
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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
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
Ying, Z., Niu, H., Gupta, P., Mahajan, D., Ghadiyaram, D., & Bovik, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00363 · Cited by 406
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-citationPrint Defect Mapping with Semantic Segmentation
Valente, A. C., Wada, C., Neves, D., Neves, D., Perez, F. V. M., Megeto, G. A. S., Cascone, M. H., Gomes, O., & Lin, Q. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093470 · Cited by 16
“In contrast to that, having no ground truth image to establish quality criteria makes NR a more challenging problem, which has seen more recent advances with the rise of deep learning [2, 8, 10, 12].”
ResultHelping Visually Impaired People Take Better Quality Pictures
Mandal, M., Ghadiyaram, D., Gurari, D., & Bovik, A. C. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3282067 · Cited by 14
Metadata
- Authors
- Kim, J., & Lee, S.
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2017-02
- Volume
- 11
- Issue
- 1
- Pages
- 206-220
- ISSN
- 1932-4553 (ISSN-L)
- 1941-0484
- OpenAlex
- W2566149141
- Cited by
- 459
- References
- 59
- FWCI
- 26.47
- Type
- journal article
- Language
- en
- Open access
- closed
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