Towards prediction of immersive Virtual Reality image quality of experience and quality of service

Anil Kumar Karembai, Jeffrey Thompson, Patrick Seeling

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 R2 = 0.68 or based on profiles with R2 = 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.

Original languageEnglish
Article number63
JournalFuture Internet
Volume10
Issue number7
DOIs
StatePublished - Jul 7 2018

Keywords

  • Electroencephalography
  • Image quality
  • Quality of experience
  • Quality of service
  • Virtual reality

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