## Experimental Results
### Direct Encoding and Manipulation  
A different set of experiments was carried out to assess the framework in a networked environment using WebVR/XR technologies between a remote group of students having different backgrounds and one analyst, all using common web browsers to interact with the 3D scene. After the sessions we also performed basic 2D image operations to measure users' performance with respect to specific target locations. The setup included:  
1. one HMD workstation for eight students to explore a sample scene _Picture Gallery_, created by Hallwyl Museum (Stockholm, Sweden) and available online on SketchFab. using the WebVR/XR online front-end (using Firefox web browser--see Figure 9, top row).
2. one server node serving 3D content and encoding incoming user states.  
Figure 8: Progressively decoding \(\sigma_{p}\) (location persistence over time) from **A** to **D** on the GPU at runtime.  
(C) one workstation where the remote analyst could inspect ongoing sessions, visualizing running QSAs and saliency tables generated by (A) in real-time.  
We involved eight participants having different backgrounds, to differentiate spatial results and compare interaction patterns:  
* 4 archaeologists.
* 2 art historians.
* 1 architect.
* 1 computer scientist.  
Students had to carry out short explorative sessions using a HMD (Oculus Rift CV1) in the sample scene (see Figure 9, top) through a WebVR/XR-enabled workstation connected to the encoding node deployed on a server. A common teleport technique (see [40]) was adopted for HMD participants allowing them to move around the scene using a single button on the VR controllers. Due to the restricted number of users, the compression ratio of QSAs against binary format (single byte precision) in this case was less pronounced: 56% regarding focus (\(QSA_{f}\)) and 29% regarding locomotion (\(QSA_{p}\)). The higher compression for locomotion is explained by the teleport interface adopted, resulting in a \"blocky\" \(QSA_{p}\) (see Figure 10) compared to focus location \(QSA_{f}\), that exhibits more frequent variations (HMD motions to observe different details of the scene).  
During the sessions, a remote analyst was using a third workstation to visually inspect generated QSAs and saliency tables on the encoding node (see Figure 9, middle and bottom strips) using a common browser (Chrome). Specifically, evolving saliency tables for locomotion fixations \(\sigma_{p}\) and focus \(\sigma_{f}\) were useful to identify ongoing users' attention and usage of the virtual space.  
Figure 9: The sample interactive scene with immersive WebVR/XR visualization for students (**top**); interactive inspection of scene saliency: \(\sigma_{p}\) (locomotion, **middle**) and \(\sigma_{f}\) (focus, **bottom**) by remote analyst using a web browser (Chrome) while students are remotely exploring the virtual environment using head-mounted displays (HMDs).  
In order to assess proximity to specific _target locations_ (selected pictures in the gallery scene), we performed basic 2D operations directly on focus and locomotion QSAs. Since each location (voxel) inside the volume can be represented by a color, we basically performed image subtraction (color distance) on both \(QSA_{f}\) and \(QSA_{p}\) (see Figure 10) to obtain proximity to target locations over time (A). A basic resampling filter along x-axis (time) can be applied to the image to obtain a weighted average (B) for proximity and image normalization (C) to rank overall performance for the eight students. Notice how these operations can be easily automated using offline 2D image processing algorithms or directly performed on GPUs.