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• W3213424031 startingPage "2768" @default.
• W3213424031 abstract "No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases." @default.
• W3213424031 created "2021-11-22" @default.
• W3213424031 creator A5000796892 @default.
• W3213424031 date "2021-11-12" @default.
• W3213424031 modified "2023-09-26" @default.
• W3213424031 title "No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features" @default.
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• W3213424031 doi "https://doi.org/10.3390/electronics10222768" @default.
• W3213424031 hasPublicationYear "2021" @default.
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