The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning
Open accessAbstract
No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms have been leveraged from natural scene statistics to deep neural networks in previous methods, the performance bottleneck still exists. In this work, we propose a hallucination-guided quality regression network to address the issue. We firstly generate a hallucinated reference constrained on the distorted image, to compensate the absence of the true reference. Then, we pair the information of hallucinated reference with the distorted image, and forward them to the regressor to learn the perceptual discrepancy with the guidance of an implicit ranking relationship within the generator, and therefore produce the precise quality prediction. To demonstrate the effectiveness of our approach, comprehensive experiments are evaluated on four popular image quality assessment benchmarks. Our method significantly outperforms all the previous state-of-the-art methods by large margins. The code and model are publicly available on the project page https://kwanyeelin.github.io/projects/HIQA/HIQA.html.
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Deep Residual Learning for Image Recognition
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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Conditional Generative Adversarial Nets
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No-Reference Image Quality Assessment in the Spatial Domain
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Context Encoders: Feature Learning by Inpainting
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Generative Adversarial Networks
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“IQA serves as a key component in the low-level computer vision community and has a wide range of applications [13, 26, 49].”
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Cited by
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00296 · Cited by 599
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
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MTD-Net: Learning to Detect Deepfakes Images by Multi-Scale Texture Difference
Yang, J., Li, A., Xiao, S., Lu, W., & Gao, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3102487 · Cited by 184
UniForensics: Face Forgery Detection via General Facial Representation
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Metadata
- Authors
- Lin, K.-Y., & Wang, G.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 732-741
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964065910
- Cited by
- 265
- References
- 11
- FWCI
- 8.62
- Type
- conference paper
- Language
- en
- Open access
- green
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