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

Anil Kumar Karembai, Jeffrey Thompson and Patrick Seeling
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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

Future Internet, 2018, vol. 10, issue 7, 1-12

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.

Keywords: virtual reality; quality of experience; quality of service; electroencephalography; image quality (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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