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Image Process."},{"key":"10.1016\/j.displa.2026.103494_b78","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/TBC.2018.2816783","article-title":"Blind image quality estimation via distortion aggravation","author":"Min","year":"2018","journal-title":"IEEE Trans. Broadcast. (TBC)"},{"key":"10.1016\/j.displa.2026.103494_b79","first-page":"2049","article-title":"Blind quality assessment based on pseudo-reference image","author":"Min","year":"2017","journal-title":"IEEE Trans. Multimed. (TMM)"},{"key":"10.1016\/j.displa.2026.103494_b80","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"10.1016\/j.displa.2026.103494_b81","doi-asserted-by":"crossref","unstructured":"W. Xue, L. Zhang, X. 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