Abstract:As Augmented Reality (AR) applications become commonplace, the determination of a device operator's subjective Quality of Experience (QoE) in addition to objective Quality of Service (QoS) metrics gains importance. Human subject experimentation is common for QoE relationship determinations due to the subjective nature of the QoE. In AR scenarios, the overlay of displayed content with the real world adds to the complexity. We employ Electroencephalography (EEG) measurements as the solution to the inherent subjectivity and situationality of AR content display overlaid with the real world. Specifically, we evaluate prediction performance for traditional image display (AR) and spherical/immersive image display (SAR) for the QoE and underlying QoS levels. Our approach utilizing a four-position EEG wearable achieves high levels of accuracy. Our detailed evaluation of the available data indicates that less sensors would perform almost as well and could be integrated into future wearable devices. Additionally, we make our Visual Interface Evaluation for Wearables (VIEW) datasets from human subject experimentation publicly available and describe their utilization.

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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.