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- W4381552821 abstract "Owing to the proliferation of user-generated videos on the Internet, blind video quality assessment (BVQA) at the edge attracts growing attention. The usage of deep-learning-based methods is restricted by their large model sizes and high computational complexity. In light of this, a novel lightweight BVQA method called GreenBVQA is proposed in this work. GreenBVQA features a small model size, low computational complexity, and high performance. Its processing pipeline includes: video data cropping, unsupervised representation generation, supervised feature selection, and mean-opinion-score (MOS) regression and ensembles. We conduct experimental evaluations on three BVQA datasets and show that GreenBVQA can offer state-of-the-art performance in PLCC and SROCC metrics while demanding significantly smaller model sizes and lower computational complexity. Thus, GreenBVQA is well-suited for edge devices." @default.
- W4381552821 created "2023-06-22" @default.
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- W4381552821 date "2023-06-17" @default.
- W4381552821 modified "2023-10-03" @default.
- W4381552821 title "Blind Video Quality Assessment at the Edge" @default.
- W4381552821 doi "https://doi.org/10.48550/arxiv.2306.10386" @default.
- W4381552821 hasPublicationYear "2023" @default.
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