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Video Technol."},{"issue":"4","key":"10.1016\/j.displa.2026.103357_b63","doi-asserted-by":"crossref","first-page":"2484","DOI":"10.1109\/TCSVT.2023.3299328","article-title":"Blind image quality assessment based on separate representations and adaptive interaction of content and distortion","volume":"34","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.displa.2026.103357_b64","doi-asserted-by":"crossref","unstructured":"B. Hu, W. Chen, C. Li, J. Leng, W. Li, X. Gao, Bidirectional Reference Image Quality Assessment via Content-Quality Correlation Modeling, in: IEEE International Conference on Acoustics, Speech and Signal Processing, 2025.","DOI":"10.1109\/ICASSP49660.2025.10889984"},{"key":"10.1016\/j.displa.2026.103357_b65","doi-asserted-by":"crossref","unstructured":"M.M.R. Mithila, M.C. 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Image Process."},{"issue":"1","key":"10.1016\/j.displa.2026.103357_b68","article-title":"Most apparent distortion: full-reference image quality assessment and the role of strategy","volume":"19","author":"Larson","year":"2010","journal-title":"J. Electron. Imaging"},{"key":"10.1016\/j.displa.2026.103357_b69","doi-asserted-by":"crossref","DOI":"10.1016\/j.image.2014.10.009","article-title":"Image database TID2013: Peculiarities, results and perspectives","volume":"30","author":"Ponomarenko","year":"2015","journal-title":"Signal Process., Image Commun."},{"key":"10.1016\/j.displa.2026.103357_b70","doi-asserted-by":"crossref","unstructured":"H. Lin, V. Hosu, D. Saupe, KADID-10k: A large-scale artificially distorted IQA database, in: International Conference on Quality of Multimedia Experience (QoMEX), 2019, pp. 1\u20133.","DOI":"10.1109\/QoMEX.2019.8743252"},{"issue":"1","key":"10.1016\/j.displa.2026.103357_b71","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1109\/TIP.2015.2500021","article-title":"Massive online crowdsourced study of subjective and objective picture quality","volume":"25","author":"Ghadiyaram","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.displa.2026.103357_b72","doi-asserted-by":"crossref","first-page":"4041","DOI":"10.1109\/TIP.2020.2967829","article-title":"KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment","volume":"29","author":"Hosu","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.displa.2026.103357_b73","doi-asserted-by":"crossref","unstructured":"Y. Fang, H. Zhu, Y. Zeng, K. Ma, Z. Wang, Perceptual quality assessment of smartphone photography, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3677\u20133686.","DOI":"10.1109\/CVPR42600.2020.00373"},{"issue":"1","key":"10.1016\/j.displa.2026.103357_b74","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TCSVT.2018.2886771","article-title":"Blind image quality assessment using a deep bilinear convolutional neural network","volume":"30","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.displa.2026.103357_b75","doi-asserted-by":"crossref","unstructured":"T. Suzuki, Superpixel segmentation via convolutional neural networks with regularized information maximization, in: IEEE International Conference on Acoustics, Speech and Signal Processing, 2020, pp. 2573\u20132577.","DOI":"10.1109\/ICASSP40776.2020.9054140"},{"key":"10.1016\/j.displa.2026.103357_b76","doi-asserted-by":"crossref","unstructured":"Y. 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