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• W4385805102 abstract "In this work we propose a novel method to evaluate the quality of enhanced videos. Perceived quality of a video depends on both technical aspects, such as the presence of distortions like noise and blur, and non-technical factors, such as content preference and recommendation. Our approach involves the use of three deep learning based models that encode video sequences in terms of their overall technical quality, quality-related attributes, and aesthetic quality. The resulting feature vectors are adaptively combined and used as input to a Support Vector Regressor to estimate the video quality score. Quantitative results on the recently released VQA Dataset for Perceptual Video Enhancement (VDPVE) introduced for the NTIRE 2023 Quality Assessment of Video Enhancement Challenge demonstrates the effectiveness of the proposed method." @default.
• W4385805102 created "2023-08-15" @default.
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• W4385805102 date "2023-06-01" @default.
• W4385805102 modified "2023-10-14" @default.
• W4385805102 title "Quality assessment of enhanced videos guided by aesthetics and technical quality attributes" @default.
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• W4385805102 doi "https://doi.org/10.1109/cvprw59228.2023.00156" @default.
• W4385805102 hasPublicationYear "2023" @default.
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