| KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment V Hosu, H Lin, T Sziranyi, D Saupe IEEE Transactions on Image Processing 29, 4041-4056, 2020 | 653* | 2020 |
| KADID-10k: A large-scale artificially distorted IQA database H Lin, V Hosu, D Saupe 2019 Eleventh International Conference on Quality of Multimedia Experience …, 2019 | 444 | 2019 |
| The Konstanz natural video database (KoNViD-1k) V Hosu, F Hahn, M Jenadeleh, H Lin, H Men, T Szirányi, S Li, D Saupe 2017 Ninth international conference on quality of multimedia experience …, 2017 | 331 | 2017 |
| Effective aesthetics prediction with multi-level spatially pooled features V Hosu, B Goldlucke, D Saupe proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 179 | 2019 |
| KonVid-150k: A dataset for no-reference video quality assessment of videos in-the-wild F Götz-Hahn, V Hosu, H Lin, D Saupe IEEE Access 9, 72139-72160, 2021 | 67* | 2021 |
| DeepFL-IQA: Weak supervision for deep IQA feature learning H Lin, V Hosu, D Saupe arXiv preprint arXiv:2001.08113, 2020 | 61 | 2020 |
| Crowd workers proven useful: A comparative study of subjective video quality assessment D Saupe, F Hahn, V Hosu, I Zingman, M Rana, S Li | 52 | 2016 |
| SUR-Net: Predicting the satisfied user ratio curve for image compression with deep learning C Fan, H Lin, V Hosu, Y Zhang, Q Jiang, R Hamzaoui, D Saupe 2019 eleventh international conference on quality of multimedia experience …, 2019 | 28 | 2019 |
| Large-scale crowdsourced subjective assessment of picturewise just noticeable difference H Lin, G Chen, M Jenadeleh, V Hosu, UD Reips, R Hamzaoui, D Saupe IEEE transactions on circuits and systems for video technology 32 (9), 5859-5873, 2022 | 25 | 2022 |
| SUR-FeatNet: Predicting the satisfied user ratio curve for image compression with deep feature learning H Lin, V Hosu, C Fan, Y Zhang, Y Mu, R Hamzaoui, D Saupe Quality and User Experience 5, 1-23, 2020 | 25 | 2020 |
| Koniq++: Boosting no-reference image quality assessment in the wild by jointly predicting image quality and defects S Su, V Hosu, H Lin, Y Zhang, D Saupe The 32nd British Machine Vision Conference, 2021 | 24 | 2021 |
| Disregarding the big picture: Towards local image quality assessment O Wiedemann, V Hosu, H Lin, D Saupe 2018 Tenth international conference on quality of multimedia experience …, 2018 | 22 | 2018 |
| Evolgan: Evolutionary generative adversarial networks B Roziere, F Teytaud, V Hosu, H Lin, J Rapin, M Zameshina, O Teytaud Proceedings of the Asian Conference on Computer Vision, 2020 | 21 | 2020 |
| Expertise screening in crowdsourcing image quality V Hosu, H Lin, D Saupe 2018 Tenth international conference on quality of multimedia experience …, 2018 | 19 | 2018 |
| Going the extra mile in face image quality assessment: A novel database and model S Su, H Lin, V Hosu, O Wiedemann, J Sun, Y Zhu, H Liu, Y Zhang, ... IEEE Transactions on Multimedia, 2023 | 15 | 2023 |
| Jpeg aic-3 dataset: towards defining the high quality to nearly visually lossless quality range M Testolina, V Hosu, M Jenadeleh, D Lazzarotto, D Saupe, T Ebrahimi 2023 15th International Conference on Quality of Multimedia Experience …, 2023 | 11 | 2023 |
| Tarsier: Evolving noise injection in super-resolution gans B Roziere, NC Rakotonirina, V Hosu, A Rasoanaivo, H Lin, C Couprie, ... 2020 25th International Conference on Pattern Recognition (ICPR), 7028-7035, 2021 | 11 | 2021 |
| Foveated video coding for real-time streaming applications O Wiedemann, V Hosu, H Lin, D Saupe 2020 Twelfth International Conference on Quality of Multimedia Experience …, 2020 | 11 | 2020 |
| Saliency-driven image coding improves overall perceived JPEG quality V Hosu, F Hahn, O Wiedemann, SH Jung, D Saupe 2016 Picture Coding Symposium (PCS), 1-5, 2016 | 11 | 2016 |
| Visual quality assessment for interpolated slow-motion videos based on a novel database H Men, V Hosu, H Lin, A Bruhn, D Saupe 2020 Twelfth International Conference on Quality of Multimedia Experience …, 2020 | 10 | 2020 |
Dietmar SaupeProfessor of Computer Science, University of Konstanz, Germany在 uni-konstanz.de 的电子邮件经过验证
