Video processing, compression
and quality research group

Based in MSU Graphics & Media Laboratory

References

1. Wang, Jianyi, Kelvin C. K. Chan, and Chen Change Loy. ‘Exploring CLIP for Assessing the Look and Feel of Images’. In AAAI, 2023.
2. Zhu, Hancheng, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi. ‘MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14143–52, 2020.
3. Liu, Xialei, Joost Van De Weijer, and Andrew D. Bagdanov. ‘Rankiqa: Learning from Rankings for No-Reference Image Quality Assessment’. In Proceedings of the IEEE International Conference on Computer Vision, 1040–49, 2017.
4. Su, Shaolin, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. ‘Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3667–76, 2020.
5. Hosu, Vlad, Hanhe Lin, Tamas Sziranyi, and Dietmar Saupe. ‘KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment’. IEEE Transactions on Image Processing 29 (2020): 4041–56.
6. Chen, Baoliang, Lingyu Zhu, Chenqi Kong, Hanwei Zhu, Shiqi Wang, and Zhu Li. ‘No-Reference Image Quality Assessment by Hallucinating Pristine Features’. IEEE Transactions on Image Processing 31 (2022): 6139–51.
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9. Dingquan Li, Tingting Jiang, and Ming Jiang. ‘Unified quality assessment of in-the-wild videos with mixed datasets training’. International Journal of Computer Vision, 129:1238–1257, 2021.
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11. Dingquan Li, Tingting Jiang, and Ming Jiang. ‘Quality assessment of in-the-wild videos’. In Proceedings of the 27th ACM International Conference on Multimedia, pages 2351–2359, 2019
12. Ying, Zhenqiang, Haoran Niu, Praful Gupta, Dhruv Mahajan, Deepti Ghadiyaram, and Alan Bovik. ‘From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3575–85, 2020.
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15. Yang, Sidi, Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujiu Yang. ‘Maniqa: Multi-Dimension Attention Network for No-Reference Image Quality Assessment’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1191–1200, 2022.
16. Seyed Mehdi Ayyoubzadeh and Ali Royatg. ‘An Attention-based Siamese-Difference Neural Network with Surrogate Ranking Loss function for Perceptual Image Quality Assessment’. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021.
17. Mittal, Anish and Moorthy, Anush Krishna and Bovik, Alan Conrad. ‘No-Reference Image Quality Assessment in the Spatial Domain’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4695-4708, 2012.
18. Zheng, Heliang and Fu, Jianlong and Zeng, Yanhong and Zha, Zheng-Jun and Luo, Jiebo. ‘Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment’. In International Conference on Computer Vision, 2021.
19. Yin, Guanghao and Wang, Wei and Yuan, Zehuan and Han, Chuchu and Ji, Wei and Sun, Shouqian and Wang, Changhu. ‘Content-Variant Reference Image Quality Assessment via Knowledge Distillation’. In the Association of the Advancement of Artificial Intelligence, 2022.
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30. Shanshan Lao, Yuan Gong, Shuwei Shi, Sidi Yang, Tianhe Wu, Jiahao Wang, Weihao Xia, and Yujiu Yang. ‘Attentions help cnns see better: Attention-based hybrid image quality assessment network’. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1140-1149, 2022.
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32. Junjie Ke, Qifei Wang, Yilin Wang, Peyman Milanfar, and Feng Yang. ‘Musiq: Multi-scale image quality transformer’. In Proceedings of the International Conference on Computer Vision, 2021.
33. A. Mittal, R. Soundararajan, and A.C. Bovik. ‘Making a ‘Completely Blind’ Image Quality Analyzer’. In IEEE Signal Processing Letters, 2013.
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