# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 1 Introduction and Related Works  
Virtual Reality (VR) is moving swiftly into main content consumption scenarios. With affordable device solutions, the optimized delivery of immersive multimedia content becomes an important aspect for network and service providers alike. Recent years have seen a vast emergence of wearable display devices, such as for Virtual Reality (VR) and Augmented Reality (AR) services. This development considering the hardware has been flanked by increased numbers of offerings for software and multimedia content that target these new device types. 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. We consider this trajectory by employing only a small number of dry electrodes with commercially available off-the-shelf equipment (in contrast to most prior research utilizing specialized EEG equipment in clinical settings). Commonly, a trade-off exists between the amount of compressed media data and the possible quality that can be attained for presentation. Readily, objectively measurable metrics can be utilized to derive network and service evaluations, commonly referred to as Quality of Service (QoS). As most of these wearable devices might rely on secondary Internet connectivity over wireless links, such as cellular connected mobile phones forwarding data [3], the delivery of networked content represents a major challenge. Recently, the focus has been changing towards low-latency first [4]. QoS metrics,such as bandwidth, latency or buffer starvation, however, typically do not accumulate directly with respect to the experience of the users of multimedia services. From a resource utilization point of view, network and service providers have an interest in optimizing the quality-data relationship for their offerings. Providers, however, also have a strong motivation to determine the actual experience of their users, as that experience normally correlates with the willingness to pay for a service offering. Subsequently, recent years have seen the emergence and shift to this user-centric Quality of Experience (QoE) [5]. The replacement of the objective QoS results in new challenges resulting from the subjective nature of the QoE, which typically requires human subject experimentations. The experiments commonly require subjects to rate their individual experience with a service on a Likert-type scale, e.g., employing a rating from worst (one) to best (five). The derivation of generalizable QoE values in the form of a ground truth is subsequently performed by repeating this experiment with a sufficiently large number of subjects. The individual ratings are subsequently aggregated and expressed in terms of Mean Opinion Scores (MOS); see, e.g., [6; 7]. The allure of the experience-based service rating in the real world and close to real-time scenarios, on the other hand, proves challenging. Human subject experimentation, however, is not easily undertaken and commonly requires approved procedures and more experimental effort than computer-driven experiments or simulations [8]. Similarly, it is not always possible to directly determine subject-specific ratings though interventions, i.e., having an active human-in-the-loop approach. The challenge of determining the QoE has subsequently led to research that results in indirectly determining the QoE from QoS parameters; see, e.g., [9; 10]. For still images, objective image quality metrics that approximate the QoE through various features have been developed for some time [11].