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KADID-10k: A Large-scale Artificially Distorted IQA Database

H. Lin, V. Hosu, and D. Saupe. Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), page 1-3. IEEE, (2019)
DOI: 10.1109/QoMEX.2019.8743252

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Technical Report on Visual Quality Assessment for Frame Interpolation.H. Men, H. Lin, V. Hosu, D. Maurer, A. Bruhn, and D. Saupe. CoRR, (2019)From Technical to Aesthetics Quality Assessment and Beyond: Challenges and PotentialV. Hosu, D. Saupe, B. Goldluecke, W. Lin, W. Cheng, J. See, and L. Wong. Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends, page 19–20. New York, NY, USA, Association for Computing Machinery, (2020)EvolGAN: Evolutionary Generative Adversarial NetworksB. Roziere, F. Teytaud, V. Hosu, H. Lin, J. Rapin, M. Zameshina, and O. Teytaud. Proceedings of the Asian Conference on Computer Vision (ACCV), (November 2020)Going the Extra Mile in Face Image Quality Assessment: A Novel Database and ModelS. Su, H. Lin, V. Hosu, O. Wiedemann, J. Sun, Y. Zhu, H. Liu, Y. Zhang, and D. Saupe. CoRR, (2022)Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable DifferenceH. Lin, G. Chen, M. Jenadeleh, V. Hosu, U. Reips, R. Hamzaoui, and D. Saupe. IEEE Transactions on Circuits and Systems for Video Technology, 32 (9): 5859-5873 (2022)EvolGAN: Evolutionary Generative Adversarial NetworksB. Roziere, F. Teytaud, V. Hosu, H. Lin, J. Rapin, M. Zameshina, and O. Teytaud. Computer Vision -- ACCV 2020, page 679--694. Cham, Springer International Publishing, (November 2021)Evolutionary Super-ResolutionB. Roziere, N. Rakotonirina, V. Hosu, H. Lin, A. Rasoanaivo, O. Teytaud, and C. Couprie. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, page 151–152. New York, NY, USA, Association for Computing Machinery, (2020)Tarsier: Evolving Noise Injection in Super-Resolution GANsB. Roziere, N. Rakotonirina, V. Hosu, A. Rasoanaivo, H. Lin, C. Couprie, and O. Teytaud. 2020 25th International Conference on Pattern Recognition (ICPR), page 7028-7035. (2021)KonIQ-10k : An Ecologically Valid Database for Deep Learning of Blind Image Quality AssessmentV. Hosu, H. Lin, T. Sziranyi, and D. Saupe. IEEE Transactions on Image Processing, (2020)Visual Quality Assessment for Motion Compensated Frame InterpolationH. Men, H. Lin, V. Hosu, D. Maurer, A. Bruhn, and D. Saupe. 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Piscataway, NJ, IEEE, (2019)

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KADID-10k: A Large-scale Artificially Distorted IQA Database

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KADID-10k: A Large-scale Artificially Distorted IQA Database