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Konx: cross-resolution image quality assessment

O. Wiedemann, V. Hosu, S. Su, and D. Saupe. Quality and User Experience, (August 2023)
DOI: 10.1007/s41233-023-00061-8

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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)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)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. IEEE Transactions on Multimedia, (2024)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)KonIQ-10k : An Ecologically Valid Database for Deep Learning of Blind Image Quality AssessmentV. Hosu, H. Lin, T. Szirányi, and D. Saupe. IEEE Transactions on Image Processing, (2020)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)Disregarding the Big Picture: Towards Local Image Quality Assessment.O. Wiedemann, V. Hosu, H. Lin, and D. Saupe. Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), page 1-6. IEEE, (2018)Critical Analysis on the Reproducibility of Visual Quality Assessment Using Deep FeaturesF. Götz-Hahn, V. Hosu, and D. Saupe. PLoS ONE, (2022)Article Number: e0269715.Saliency-driven Image Coding Improves Overall Perceived JPEG QualityV. Hosu, F. Hahn, O. Wiedemann, S. Jung, and D. Saupe. Proceedings of the Picture Coding Symposium (PCS), page 1-5. IEEE, (2016)

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Konx: cross-resolution image quality assessment

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Konx: cross-resolution image quality assessment

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Konx: cross-resolution image quality assessment