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Open Access NARVAL: A no-reference video quality tool for real-time communications

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In this paper we introduce two new no-reference metrics and compare their performance to state-of-the-art metrics on six publicly available datasets having a large variety of distortions and characteristics. Our two metrics, based on neural networks, combine the following features: histogram of oriented gradients, edges detection, fast fourier transform, CPBD, blur and contrast measurement, temporal information, freeze detection, BRISQUE and Video BLIINDS. They perform better than Video BLIINDS and BRISQUE on the six datasets used in this study, including one made up of natural videos that have not been artificially distorted. Our metrics show a good generalization as they achieved high performance on the six datasets.
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Keywords: neural network; real-time communication; video quality metric

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "NARVAL: A no-reference video quality tool for real-time communications".

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