IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v10y2018i7p63-d156812.html
   My bibliography  Save this article

Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service

Author

Listed:
  • Anil Kumar Karembai

    (Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA)

  • Jeffrey Thompson

    (Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA)

  • Patrick Seeling

    (Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA)

Abstract

In this article, we evaluate the Quality of Service (QoS) through media impairment levels and device operators’ subjective Quality of Experience (QoE). The human-centered QoE determination commonly requires human subject experimentation, which we combine with Electroencephalography (EEG) measurements to move towards automatized and generalized possibilities of determining the QoE. We evaluate the prediction performance for spherical/immersive images displayed with a mobile device VR viewer (Spherical Virtual Reality (SVR)) with the help of only four-position EEG data gathered at the forehead, which correlates well with practical applicability. We find that QoS levels can be predicted more reliably (directly with R 2 = 0.68 or based on profiles with R 2 = 0.9 ) than the QoE, which exhibits significant error levels. Additional comparison with previous approaches for the Spherical Augmented Reality (SAR) QoE indicates better predictability in AR scenarios over VR.

Suggested Citation

  • Anil Kumar Karembai & Jeffrey Thompson & Patrick Seeling, 2018. "Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service," Future Internet, MDPI, vol. 10(7), pages 1-12, July.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:7:p:63-:d:156812
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/10/7/63/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/10/7/63/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brian Bauman & Patrick Seeling, 2017. "Visual Interface Evaluation for Wearables Datasets: Predicting the Subjective Augmented Vision Image QoE and QoS," Future Internet, MDPI, vol. 9(3), pages 1-13, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jesus GomezRomero-Borquez & J. Alberto Del Puerto-Flores & Carolina Del-Valle-Soto, 2023. "Mapping EEG Alpha Activity: Assessing Concentration Levels during Player Experience in Virtual Reality Video Games," Future Internet, MDPI, vol. 15(8), pages 1-14, August.
    2. Vitalii Beschastnyi & Daria Ostrikova & Roman Konyukhov & Elizaveta Golos & Alexander Chursin & Dmitri Moltchanov & Yuliya Gaidamaka, 2021. "Quantifying the Density of mmWave NR Deployments for Provisioning Multi-Layer VR Services," Future Internet, MDPI, vol. 13(7), pages 1-16, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:10:y:2018:i:7:p:63-:d:156812. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.