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• W2017336566 abstract "In this work, we study the visual quality of streaming video and propose a fusion-based video quality assessment (FVQA) index to predict its quality. In the first step, video sequences are grouped according to their content complexity to reduce content diversity within each group. Then, at the second step, several existing video quality assessment methods are fused to provide the final video quality score, where fusion coefficients are learned from training video samples in the same group. We demonstrate the superior performance of FVQA as compared with other video quality assessment methods using the MCL-V video quality database." @default.
• W2017336566 created "2016-06-24" @default.
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• W2017336566 date "2014-12-01" @default.
• W2017336566 modified "2023-09-30" @default.
• W2017336566 title "A fusion-based video quality assessment (fvqa) index" @default.
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• W2017336566 doi "https://doi.org/10.1109/apsipa.2014.7041705" @default.
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