Blind Image Quality Assessment Method Based on DeepSA-Net
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50069-5_11
Reference28 articles.
1. Niu, Y., Huang, D., Shi, Y., et al.: Siamese-network-based learning to rank for no-reference 2D and 3D image quality assessment. IEEE Access 7, 101583–101595 (2019)
2. Su, S., Yan, Q., Zhu, Y., et al.: Blindly assess image quality in the wild guided by a self-adaptive hyper network. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020)
3. Feng, Y., Li, S., Chang, Y.: Multi-scale feature-guided stereoscopic video quality assessment based on 3D convolutional neural network. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada. IEEE (2021)
4. He, L., Zhong, Y., Lu, W., et al.: A visual residual perception optimized network for blind image quality assessment. IEEE Access 7, 176087–176098 (2019)
5. Ma, J., Wu, J., Li, L., et al.: Active inference of GAN for no-reference image quality assessment. In: 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE (2020)
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