| Waterloo exploration database: New challenges for image quality assessment models K Ma, Z Duanmu, Q Wu, Z Wang, H Yong, H Li, L Zhang IEEE Transactions on Image Processing 26 (2), 1004-1016, 2016 | 560 | 2016 |
| End-to-end blind image quality assessment using deep neural networks K Ma, W Liu, K Zhang, Z Duanmu, Z Wang, W Zuo IEEE Transactions on Image Processing 27 (3), 1202-1213, 2017 | 441 | 2017 |
| A quality-of-experience index for streaming video Z Duanmu, K Zeng, K Ma, A Rehman, Z Wang IEEE Journal of Selected Topics in Signal Processing 11 (1), 154-166, 2016 | 187 | 2016 |
| Multi-exposure image fusion by optimizing a structural similarity index K Ma, Z Duanmu, H Yeganeh, Z Wang IEEE Transactions on Computational Imaging 4 (1), 60-72, 2017 | 177 | 2017 |
| Deep guided learning for fast multi-exposure image fusion K Ma, Z Duanmu, H Zhu, Y Fang, Z Wang IEEE Transactions on Image Processing 29, 2808-2819, 2019 | 144 | 2019 |
| A quality-of-experience database for adaptive video streaming Z Duanmu, A Rehman, Z Wang IEEE Transactions on Broadcasting 64 (2), 474-487, 2018 | 125 | 2018 |
| End-to-End Blind Quality Assessment of Compressed Videos Using Deep Neural Networks. W Liu, Z Duanmu, Z Wang ACM Multimedia, 546-554, 2018 | 113 | 2018 |
| Group mad competition-a new methodology to compare objective image quality models K Ma, Q Wu, Z Wang, Z Duanmu, H Yong, H Li, L Zhang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 100 | 2016 |
| Perceptual quality assessment of 3D point clouds H Su, Z Duanmu, W Liu, Q Liu, Z Wang 2019 IEEE International Conference on Image Processing (ICIP), 3182-3186, 2019 | 57 | 2019 |
| Group maximum differentiation competition: Model comparison with few samples K Ma, Z Duanmu, Z Wang, Q Wu, W Liu, H Yong, H Li, L Zhang IEEE Transactions on pattern analysis and machine intelligence 42 (4), 851-864, 2018 | 55 | 2018 |
| Perceptual quality assessment of colored 3D point clouds Q Liu, H Su, Z Duanmu, W Liu, Z Wang IEEE Transactions on Visualization and Computer Graphics, 2022 | 51 | 2022 |
| Quality-of-experience for adaptive streaming videos: An expectation confirmation theory motivated approach Z Duanmu, K Ma, Z Wang IEEE Transactions on Image Processing 27 (12), 6135-6146, 2018 | 50 | 2018 |
| Quality-of-experience of adaptive video streaming: Exploring the space of adaptations Z Duanmu, K Ma, Z Wang Proceedings of the 25th ACM international conference on Multimedia, 1752-1760, 2017 | 39 | 2017 |
| AVC, HEVC, VP9, AVS2 OR AV1?—A comparative study of state-of-the-art video encoders on 4K videos Z Li, Z Duanmu, W Liu, Z Wang Image Analysis and Recognition: 16th International Conference, ICIAR 2019 …, 2019 | 29 | 2019 |
| A knowledge-driven quality-of-experience model for adaptive streaming videos Z Duanmu, W Liu, D Chen, Z Li, Z Wang, Y Wang, W Gao arXiv preprint arXiv:1911.07944, 2019 | 22 | 2019 |
| Quantifying visual image quality: A Bayesian view Z Duanmu, W Liu, Z Wang, Z Wang Annual Review of Vision Science 7, 437-464, 2021 | 20 | 2021 |
| Assessing the quality-of-experience of adaptive bitrate video streaming Z Duanmu, W Liu, Z Li, D Chen, Z Wang, Y Wang, W Gao arXiv preprint arXiv:2008.08804, 2020 | 18 | 2020 |
| Quality-of-experience prediction for streaming video Z Duanmu, A Rehman, K Zeng, Z Wang 2016 IEEE International Conference on Multimedia and Expo (ICME), 1-6, 2016 | 17 | 2016 |
| Temporal motion smoothness and the impact of frame rate variation on video quality RM Nasiri, Z Duanmu, Z Wang 2018 25th IEEE International Conference on Image Processing (ICIP), 1418-1422, 2018 | 14 | 2018 |
| Geometric transformation invariant image quality assessment using convolutional neural networks K Ma, Z Duanmu, Z Wang 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 13 | 2018 |
Zhou WangProfessor, Electrical and Computer Engineering, University of WaterlooVerified email at uwaterloo.ca
