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Video Quality Assessment Considering the Features of the Human Visual System

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Image and Vision Computing (IVCNZ 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13836))

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Abstract

Nowadays, numerous video compression quality assessment metrics are available. Some of these metrics are “objective” and only tangentially represent how a human observer rates video quality. On the other hand, models of the human visual system have been shown to be effective at describing spatial coding. In this work we propose a new quality metric which extends the peak signal to noise ratio metric with features of the human visual system measured using modern LCD screens. We also analyse the current visibility models of the early visual system and compare the commonly used quality metrics with metrics containing data modelling human perception. We examine the Pearson’s linear correlation coefficient of the various video compression quality metrics with human subjective scores on videos from the publicly available Netflix data set. Of the metrics tested, our new proposed metric is found to have the most stable high performance in predicting subjective video compression quality.

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Authors and Affiliations

  1. The University of Waikato, Hamilton, New Zealand

    Anastasia Mozhaeva, Michael J. Cree & Lee Streeter

  2. Moscow Technical University of Communications and Informatics, Moscow, Russia

    Anastasia Mozhaeva & Vladimir Mazin

Authors
  1. Anastasia Mozhaeva
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  2. Vladimir Mazin
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  3. Michael J. Cree
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  4. Lee Streeter
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Corresponding author

Correspondence to Lee Streeter .

Editor information

Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  2. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  3. Auckland University of Technology, Auckland, New Zealand

    Martin Stommel

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Mozhaeva, A., Mazin, V., Cree, M.J., Streeter, L. (2023). Video Quality Assessment Considering the Features of the Human Visual System. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_21

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  • DOI: https://doi.org/10.1007/978-3-031-25825-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25824-4

  • Online ISBN: 978-3-031-25825-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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