Visual Interface Evaluation for Wearables Datasets: Predicting the Subjective Augmented Vision Image QoE and QoS

Abstract

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.

Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service

“…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].…”

“…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.…”

“…In turn, media consumption in governmental, industrial and consumer scenarios is highly likely to increasingly utilize these types of head-mounted devices. Initial interest is emerging for directly comparing the two approaches in immersive contexts [1], which we consider with our prior work from [2] in this article, as well. With the head-worn nature of VR equipment, an opportunity exists to integrate the determination of EEG data into a closed feedback loop for QoE determinations (and adjustments) in future device iterations.…”

“…Image quality assessments that are non-referential have attracted recent research efforts, as well; see, e.g., [12]. In this article, we rely on the BRISQUEimage impairment metric [13] as the QoS indicator, which we successfully employed in past research efforts [2]. A similar dataset that presents immersive VR videos is described in [14].…”

Virtual Transcendence Experiences

“…They showed that sudden changes of the speed in the translational camera motion and excessive vibration can cause cyber-sickness. Another important recent stream of activities in this direction is the real-time estimation of viewers' QoE levels exploiting physiological signals [20], [21], [22]. This category of activities aims at tracking singular or combinations of physiological responses (e.g., heart rate, electro-dermal activity, and electroencephalogram) in the course of immersive media experience.…”