# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 5 Discussion  
Throughout the prior sections, we focused on the description of the attainable results for Spherical Virtual Reality (SVR) image QoE and QoS approximations. The results illustrated in Figure 4 highlight that in almost all cases, the subject-dependent \(R^{2}\) results are worse than those obtained when considering all available EEG data. While small improvements in the average MAE can be observed for these additional configurations, the combination of MAE and \(R^{2}\) performance does not make any of these suitable for real-time QoE predictions. Overall, for the direct EEG value-based predictions of the QoE, we noted the best scenario resulting in an overall average of just above \(R^{2}=0.6\), which is promising, but it did not result in the desired low-level MAE that would make the prediction outcome directly applicable. The average MAE level approaching two indicates that a prediction effort would be off by this amount on average, e.g., predicting the rating of an average quality experience as low or high. Even the EEGz-based approach with higher \(R^{2}\) outcomes does not significantly improve on the QoE predictability, as indicated by comparably high levels of MAE. Given these outcomes, it is not surprising that a reduction in the number of sensors yields even worse results and is not feasible for reliable QoE predictions in the VR settings we considered. If we considered the impairment levels themselves as QoS as the prediction target, we observe a completely different outcome. Here, we derive a feasible approach to the prediction of the QoS level based on the EEG and EEGz values of most participating subjects. The shift to the QoS in both cases results in a significant increase in the performance, rendering the current approach applicable for a QoS approximation. Comparing the QoE and QoS MAE levels for the EEG and EEGz approaches, we note a significant difference of the QoS approach (i.e., utilizing the objective image quality metric). One of the reasons for this difference is the deviation of the QoE ratings by subjects and the previously set level. Fitting the QoE through EEG signal evaluation, thus, also incorporates these differences between set and perceived quality levels, resulting in increased errors. The commonly possible interpolation of the QoE through QoS means (e.g., following the IQXapproach in [9]) could, in turn, yield an approximation of the QoE, albeit with likely additional approximation accuracy penalties. In comparison to the results we obtained as part of our own prior research results for augmented reality (SAR) settings in [2], we find several similarities. For comparison, we provide the results for the EEG-based SAR \(R^{2}\) and MAE values in Figure 6.  
Figure 6: \(R^{2}\) and MAE results for the EEG-based prediction of the same images in the augmented binocular vision (SAR) setting; see [2]. While not presented in Section 4, the evaluation of individual EEG sensor positions results in a similar low level of \(R^{2}\) scores (and, subsequently, high MAE levels). For the EEGz data (to enable a direct comparison), we note that for all position data, the SAR exhibited an average of \(R^{2}=0.88\) for the QoE prediction, which is very comparable to the values we obtained here for the SVR. However, the major difference is a significantly lower \(MAE=0.09\) in the SAR QoE prediction scenario, which made that approach directly applicable; see Figure 6b. Similar trends were again observed for shifting from the QoE to the QoS, which results in a slight performance increase for both scenarios. We additionally note that for both scenarios, shifting the available EEG electrode data to the sides, center or outside has similar impacts on the resulting \(R^{2}\) scores and MAEs. Jointly, our results indicate overall predictability of the SVR-QoS, but not the SVR-QoE. This could enable future implementations in passive human-in-the-loop settings through approximations of the SVR-QoE from the SVR-QoS, albeit at a potential penalty in accuracy.