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

Towards Predictions of the Image Quality of Experience for Augmented Reality Scenarios