## 2 Methodology  
In this section, we highlight the generation of the dataset through experimentation before describing it in greater detail in Section 3. The general configuration for our experiments was previously described in [16; 27; 35; 36]. Overall, we follow the generation to evaluation process illustrated in Figure 1, noting that the prediction performance evaluation results are presented last for readability. Human subjects were initially introduced to the experiment and overall system utilization. All subjects gave their informed consent for inclusion before they participated in the experiments. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of Central Michigan University (Central Michigan University Institutional Review Board #568993). The participants wore a commercial-grade head-mounted binocular vision augmentation device and a commercial-grade EEG headband, both available off-the-shelf. Specifically, we employ the Epson Moverio BT-200 mobile viewer, which consists of a wearable head-mounted display unit and a processing unit that utilizes the Android Operating System. The display is wired to receive the video signals and power from the processing unit and has a resolution of 960 by 540 pixels with Light Emitting Diode (LED) light sources and a 23-degree Field Of View (FOV). The display reproduces colors  
Figure 1: Overview of the methodology for creating and evaluating the Visual Interface Evaluation for Wearables (VIEW) datasets, including relevant sections in this paper; \(R^{2}\): Coefficient of determination; MAE: Mean Absolute Errors. at 24-bit depth at 60 Hz with 70% transparency. The real-world backdrop is a small meeting/classroom with participants facing a whiteboard initially. The room was dim, with the main light source coming from shaded windows to the side, as in prior works. The subjects (or users) \(u\) had 15 s of media viewing time ranging from \(t_{u}^{s}(i,l)\) to \(t_{u}^{e}(i,l)\), whereby we denote the image as \(i\) and the impairment level as \(l\). The presentation was followed by an unrestricted quality rating period. During the rating period, subjects were asked to rate the previously-observed media quality on a five-point Likert scale. After a short gray screen period, the next image was presented. Overall, our approach follows the Absolute Category Rating with Hidden Reference (ACR-HR) approach according to the International Telecommunication Union -Telecommunication Standardization Sector (ITU-T) P.910 [37], i.e., we include the original image in the evaluation as additional reference. The result is the subject's quality of experience for this particular presentation, denoted as \(q_{u}^{r}(i,l)\). We currently consider two different content scenarios, namely (i) traditional image display ( which we denote as AR) and (ii) spherical (immersive) image display ( which we denote as SAR). The images selected for the traditional image display condition were obtained from the Tampere Image Database from 2013 (TID2013) for reference [38], and initial findings were described in [27; 36]. Specifically, we included the JPEG compression distortion in our evaluations and the resultant dataset for the AR condition, with the reference images illustrated in Figure 2.  
Images selected for the spherical image display (SAR) condition were derived by applying different levels of JPEG compression to the source images, mimicking the impairments of the regular images. The selected spherical source images are from the Adobe Stock images library, which represent studio-quality images as a baseline. We illustrate these spherical images for reference in Figure 3.  
Figure 3: Overview of the spherical images employed in the SAR scenario; (**a**): a _Bamboo_ hut; (**b**): a view of rocks and a _Beach House_; (**c**): a backyard _Garden_; (**d**): a _Golf_ course; (**e**): a _Mosque_ and the plaza in front; (**f**): an underwater _Ocean_ scene.