# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 2 Methodology  
This section addresses the general approach to the data generation processes before describing the performance metrics employed in greater detail. Overall, our approach follows that described in prior works, specifically the Visual Interface Evaluation for Wearables (VIEW) datasets [2; 24; 25; 26]. Our methodology, thus, follows the process illustrated in Figure 1[2]. A major difference from the prior works is that here, we are not focusing on the generation of reusable datasets, but directly on the evaluation of the gathered data from the new human subject experiments. The participating human subjects were provided with an overview of the experimental flow and gave their informed consent for inclusion before they participated in the experiments. We note that the subjects employed in this study were recruited separately from prior works, but an overlap might exist (but is not discernible due to anonymity of the data gathering process). The study was conducted during the Spring semester of 2018 in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of Central Michigan University (Central Michigan University Institutional Review Board #568993). The device employed for rendering the content was an ASUS Zenphone AR smartphone, which features a 5.7-inch (2560 by 1440 pixels) Super AMOLED (Active-Matrix Organic Light-Emitting Diode) display capable of over 100% of the NTSC (National Television Standards Committee) color gamut. Participants employed a virtual reality smartphone adapter together with the phone to display the immersive content similar to current content consumption scenarios. A custom web-technology-based viewer application was executed on the phone and controlled remotely from a dedicated laptop. The overall group of human subjects that participated consisted of 4 females and 11 males with an overall average age of 23.4 years (SD = 2.97 years). The subjects were commonly recruited from the general student population on Central Michigan University's main campus and, subsequently, was comprised of non-experts. Eight of the subjects wore vision-correcting devices, whereby seven subjects chose to wear these devices during their respective experimental session. In addition, the participants wore the commercial-grade MUSE  
Figure 1: Overview of the methodology for creating and evaluating the Spherical Virtual Reality (SVR) data, as employed in [2]. Initial data gathering through a new set of human subject experiments is followed by data preparations and subsequent prediction evaluations. EEG headband (we note that detailed developer documentation for the headband is available for interested readers at [[http://www.choosemuse.com/developer/](http://www.choosemuse.com/developer/)]([http://www.choosemuse.com/developer/](http://www.choosemuse.com/developer/))). We provide a schematic overview of the on-person setup in Figure 2.  
The participating users \(u\) viewed the immersive image content \(i\) at different impairment levels \(l\) for a time ranging from \(t_{u}^{s}(i,l)\)-\(t_{u}^{e}(i,l)\). The presentation was followed by an unrestricted quality rating period, where subjects were asked to rate the previously-observed media quality on a 5-point Likert scale. After a short black screen period, the next randomly-chosen media and impairment combination was presented to the subjects. Overall, our approach follows the ACR-HR (Absolute Category Rating with Hidden Reference) approach according to ITU-T P.910 [27], i.e., we include the original image in the evaluation as an additional reference. The result is the subject's quality of experience for the Spherical (immersive) Virtual Reality content display (SVR), which we denote as \(q_{u}^{r}(i,l)\). The images selected for the spherical image display were derived by applying different levels of JPEG compression to the source images, mimicking the impairments of the regular images.