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.

Agarla, M., Celona, L., Rota, C., Schettini, R. (2023). Quality assessment of enhanced videos guided by aesthetics and technical quality attributes. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp.1533-1541). IEEE Computer Society [10.1109/CVPRW59228.2023.00156].

Quality assessment of enhanced videos guided by aesthetics and technical quality attributes

Agarla, M;Celona, L;Rota, C;Schettini, R
2023

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.
slide + paper
Video quality assessment, Enhanced videos, Transformers
English
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 - 18 June 2023 through 22 June 2023
2023
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
9798350302493
2023
2023-June
1533
1541
none
Agarla, M., Celona, L., Rota, C., Schettini, R. (2023). Quality assessment of enhanced videos guided by aesthetics and technical quality attributes. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp.1533-1541). IEEE Computer Society [10.1109/CVPRW59228.2023.00156].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/435123
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