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• W4386932480 abstract "With the rapid development of multimedia user-generated content (UGC), the number of videos characterized by multiple resolutions and frame rates has increased. The development of efficient no-reference video quality assessment (NR-VQA) models for UGC with these features is a challenging task. Although previous studies have proposed solutions that combine multi-scale spatial and multi-rate motion information, existing NR-VQA models simply connect multi-attribute features obtained separately, often leading to information loss and distortion. In this study, we developed a spatiotemporal alignment network addressing the limitations of existing methods. Our model includes two modules, one for multi-rate motion feature alignment and the other for multi-scale spatial feature alignment. Moreover, by introducing a Lagrangian multi-rate and multi-scale attention-based alignment mechanism, multiple features can be effectively processed without incurring in unwanted information conflict or loss. By associating spatial and time-series information, our attention-based feature-alignment module enhances low-quality spatial regions around subject objects, thus, improving the performance of the model significantly. Our experimental results demonstrated that the proposed model outperforms other existing methods on several benchmark datasets, proving its effectiveness and generalizability. Code will be available at: https://github.com/945402003/STAN-VQA ." @default.
• W4386932480 created "2023-09-22" @default.
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• W4386932480 date "2023-01-01" @default.
• W4386932480 modified "2023-10-16" @default.
• W4386932480 title "STAN: Spatio-Temporal Alignment Network for No-Reference Video Quality Assessment" @default.
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• W4386932480 doi "https://doi.org/10.1007/978-3-031-44213-1_14" @default.
• W4386932480 hasPublicationYear "2023" @default.
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