The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network
Abstract
Blind image quality assessment (BIQA) for authentically distorted images has always been a challenging problem, since images captured in the wild include varies contents and diverse types of distortions. The vast majority of prior BIQA methods focus on how to predict synthetic image quality, but fail when applied to real-world distorted images. To deal with the challenge, we propose a self-adaptive hyper network architecture to blind assess image quality in the wild. We separate the IQA procedure into three stages including content understanding, perception rule learning and quality predicting. After extracting image semantics, perception rule is established adaptively by a hyper network, and then adopted by a quality prediction network. In our model, image quality can be estimated in a self-adaptive manner, thus generalizes well on diverse images captured in the wild. Experimental results verify that our approach not only outperforms the state-of-the-art methods on challenging authentic image databases but also achieves competing performances on synthetic image databases, though it is not explicitly designed for the synthetic task.
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Cites
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Self-citationVery Deep Convolutional Networks for Large-Scale Image Recognition
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Cited by
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 733
MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment
Yang, S., Wu, T., Shi, S., Lao, S., Gong, Y., Cao, M., Wang, J., & Yang, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00126 · Cited by 527
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
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
Zhang, W., Zhai, G., Wei, Y., Yang, X., & Ma, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01352 · Cited by 325
Image Quality Assessment: From Mean Opinion Score to Opinion Score Distribution
Gao, Y., Min, X., Zhu, Y., Li, J., Zhang, X.-P., & Zhai, G. (2022) · Proceedings of the 30th ACM International Conference on Multimedia · doi:10.1145/3503161.3547872 · Cited by 58
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Metadata
- Authors
- Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., & Zhang, Y.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 3664-3673
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035719652
- Cited by
- 848
- References
- 49
- FWCI
- 21.98
- Type
- conference paper
- Language
- en
- Open access
- closed
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