  Augmented Reality (AR) devices are commonly head-worn to overlay
context-dependent information into the field of view of the device operators.
One particular scenario is the overlay of still images, either in a traditional
fashion, or as spherical, i.e., immersive, content. For both media types, we
evaluate the interplay of user ratings as Quality of Experience (QoE) with (i)
the non-referential BRISQUE objective image quality metric and (ii) human
subject dry electrode EEG signals gathered with a commercial device.
Additionally, we employ basic machine learning approaches to assess the
possibility of QoE predictions based on rudimentary subject data. Corroborating
prior research for the overall scenario, we find strong correlations for both
approaches with user ratings as Mean Opinion Scores, which we consider as QoE
metric. In prediction scenarios based on data subsets, we find good performance
for the objective metric as well as the EEG-based approach. While the objective
metric can yield high QoE prediction accuracies overall, it is limited i its
application for individual subjects. The subject-based EEG approach, on the
other hand, enables good predictability of the QoE for both media types, but
with better performance for regular content. Our results can be employed in
practical scenarios by content and network service providers to optimize the
user experience in augmented reality scenarios.
