## 1 Introduction  
Increasingly, wearable media display devices, such as for Virtual Reality (VR) and Augmented Reality (AR) services, become sources for media consumption in industrial and consumer scenarios. Typically, these devices perform binocular vision augmentation and content presentation, and initial interest is emerging for directly comparing the two approaches in immersive contexts [1]. For AR applications in particular, content is commonly displayed to provide context-dependent information. The goal is to positively modify human performance, e.g., for driving tasks [2], in the context of medical procedures [3; 4] or for educational purposes [5]. The delivery of context-dependent network-delivered content to devices in near real time, however, represents a challenge and requires new paradigm considerations [6]. Facilitating the content distribution to these device types can follow multiple approaches, such as direct wired or wireless connectivity or proxification with cellular connected mobile phones [7]. In either scenario, a trade-off exists between the amount of compressed media data and the possible quality that can be attained for presentation. While past research and implementation efforts were generally directed at objective metrics, typically summarized as Quality of Service (QoS), the subjective Quality of Experience (QoE) has become popular in the determination of overall service quality [8]. In turn, network and service providers have an interest in optimizing the quality-datarelationship for their offerings by taking the user's experience as QoE into account. This approach to the valuation of predominantly mobile services is beginning to attract interest in AR scenarios [9].  
The QoE, however, is commonly derived from human subject experimentations, whereby participants rate their experience on a Likert-type scale from worst to best. The individual ratings are subsequently aggregated and expressed in terms of Mean Opinion Scores (MOS); see, e.g., [10; 11]. This subjective rating approach, in turn, is based on cognitive and emotional states at the time of experimentation and combines with the actual delivery of the service under evaluation. Human subject experimentation, however, is not easily undertaken and commonly requires approved procedures and more experimental effort than computer-driven experiments or simulations [12]. The MOS approach furthermore averages the individual dependencies to derive an overall applicable relationship between the underlying service delivery and user experiences in general. An additional downside to this approach is, thus, the loss of the finer-grained subjective dependencies.  
The subjective nature of quality considerations has led to several objective image quality metrics that approximate the QoE through various features; see, e.g., [13]. Strides were made that combine the QoS and the QoE in a generalized quantitative relationship to facilitate their determination; see, e.g., [14; 15]. The main goal of these efforts is to exploit the underlying common media compression and impairment metrics (QoS) that are readily obtainable in order to closely approximate the subjective experience (QoE). This approach enables easier experiment replication and practical implementation.