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International Conference on Machine Learning and Intelligent Communications

MLICOM 2017: Machine Learning and Intelligent Communications pp 3–7Cite as

The Application of Equivalent Mean Square Error Method in Scalable Video Perceptual Quality

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Abstract

Scalable video is a stream video over heterogeneous networks to different clients. To provide the better quality of service (QoS) or quality of experience (QoE) to customer, we propose an Equivalent Mean Square Error (Eq-MSE) method which is developed based on spatial and temporal frequency analysis of input video content. Eq-MSE is used to calculate minimal frame rate (MinFR) for different videos to guarantee motion without jitter. The proposed scheme in this paper can provide better perceptual video quality than without considering the video content impact.

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References

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Author information

Authors and Affiliations

  1. Dalian Neusoft University of Information, Software Park Road 8, Dalian, 116023, Liaoning, China

    Daxing Qian, Ximing Pei & Xiangkun Li

Authors
  1. Daxing Qian
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  2. Ximing Pei
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  3. Xiangkun Li
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Corresponding author

Correspondence to Daxing Qian .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Cite this paper

Qian, D., Pei, X., Li, X. (2018). The Application of Equivalent Mean Square Error Method in Scalable Video Perceptual Quality. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-73447-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73446-0

  • Online ISBN: 978-3-319-73447-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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