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• W3138613675 abstract "Methods for No-Reference Video Quality Assessment (NR-VQA) of consumer-produced video content are largely investigated due to the spread of databases containing videos affected by natural distortions. In this work, we design an effective and efficient method for NR-VQA. The proposed method exploits a novel sampling module capable of selecting a predetermined number of frames from the whole video sequence on which to base the quality assessment. It encodes both the quality attributes and semantic content of video frames using two lightweight Convolutional Neural Networks (CNNs). Then, it estimates the quality score of the entire video using a Support Vector Regressor (SVR). We compare the proposed method against several relevant state-of-the-art methods using four benchmark databases containing user generated videos (CVD2014, KoNViD-1k, LIVE-Qualcomm, and LIVE-VQC). The results show that the proposed method at a substantially lower computational cost predicts subjective video quality in line with the state of the art methods on individual databases and generalizes better than existing methods in cross-database setup." @default.
• W3138613675 created "2021-03-29" @default.
• W3138613675 creator A5028039581 @default.
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• W3138613675 date "2021-03-13" @default.
• W3138613675 modified "2023-09-29" @default.
• W3138613675 title "An Efficient Method for No-Reference Video Quality Assessment" @default.
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• W3138613675 doi "https://doi.org/10.3390/jimaging7030055" @default.
• W3138613675 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8321305" @default.
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