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Self-similar Nature of 3D Video Formats

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Multiple Access Communications (MACOM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 8715))

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Abstract

Communication network traffic shows self-similar behavior with long-range dependency (LRD). We analyzed self-similar nature of different 3D video formats, namely the multiview (MV) video, the frame sequential (FS) and the side-by-side (SBS) representation video formats using the visual and more rigorous methods. Analyses were performed by the aggregated variance method, the R/S statistics method, and the multifractal based multiscale method. We used publicly available long full HD video frame-size traces of 3D videos. Additionally, self-similarity of 3D videos with streaming approach with aggregation of frames is showed.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, University of Belgrade, Serbia

    Amela Zeković & Irini Reljin

  2. School of Electrical and Computer Engineering of Applied Studies, Belgrade, Serbia

    Amela Zeković

Authors
  1. Amela Zeković
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  2. Irini Reljin
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Editor information

Editors and Affiliations

  1. School of Information Science, Computer and Electrical Engineering, Halmstad University, Kristian IV:s väg 3, 30118, Halmstad, Sweden

    Magnus Jonsson  & Alexey Vinel  & 

  2. Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, 08018, Barcelona, Spain

    Boris Bellalta

  3. Department of Electronics and Communications Engineering, Tampere University of Technology, Korkeakoulunkatu 3, 33720, Tampere, Finland

    Evgeny Belyaev

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Zeković, A., Reljin, I. (2014). Self-similar Nature of 3D Video Formats. In: Jonsson, M., Vinel, A., Bellalta, B., Belyaev, E. (eds) Multiple Access Communications. MACOM 2014. Lecture Notes in Computer Science, vol 8715. Springer, Cham. https://doi.org/10.1007/978-3-319-10262-7_10

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  • DOI: https://doi.org/10.1007/978-3-319-10262-7_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10261-0

  • Online ISBN: 978-3-319-10262-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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