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• W4311885993 abstract "Intelligent video surveillance (IVS) technology is widely used in various security systems. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based tasks, leading to the difficulties in the IVS system extracting valid information from SIs. In this paper, we propose a hybrid no-reference image quality assessment (NR IQA) model for SIs that can help to identify undesired distortions and provide useful guidelines for IVS technology. Specifically, we first extract two main types of quality-aware features: the low-level visual features related to various distortions, and the high-level semantic information, which is extracted by a state-of-the-art (SOTA) vision transformer backbone. Then, we fuse these two kinds of features into the final quality-aware feature vector, which is mapped into the quality index through the feature regression module. Our experimental results on two surveillance content quality databases demonstrate that the proposed model achieves the best performance compared to the SOTA on NR IQA metrics." @default.
• W4311885993 created "2023-01-02" @default.
• W4311885993 creator A5019288987 @default.
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• W4311885993 date "2022-12-16" @default.
• W4311885993 modified "2023-10-18" @default.
• W4311885993 title "Hybrid No-Reference Quality Assessment for Surveillance Images" @default.
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• W4311885993 doi "https://doi.org/10.3390/info13120588" @default.
• W4311885993 hasPublicationYear "2022" @default.
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