The appearance mode of an object, whether it appears selfluminous or reflective, depends on its luminance and its surrounding. This research aims to verify whether the appearance mode of a spherical lamp ("on" / "off") and perceived room brightness is influenced by the presentation medium: real 3D scenes (R-3D), rendered virtual 3D scenes (VR-3D) presented on a head-mounted-display (HMD) and 2D scenes presented on a regular display (D-2D). Twenty observers evaluated the lamp's appearance mode when presented in different luminance values and rated the apparent room brightness of the scene under four viewing conditions: R3D and D-2D with warm-white scene lighting, and D-2D and VR-3D with cool-white scene lighting. Threshold luminance, defined as the luminance corresponding to a 50-50 chance of perceiving a lamp as switched on, showed large observer variability, which might originate from the diversity of the observers' understanding of the lamp material and their strategy to judge the appearance mode. Respectively, threshold luminance and room brightness were significantly lower and significantly higher for the virtual reality scene than for the other conditions. However, no evidence was found that the appearance mode of a spherical lamp can relevantly predict room brightness.
With the development of virtual reality (VR) and related technologies, the establishment of immersion calls for higher quality of panoramic video contents. However, the processing on the videos greatly influences the quality. Therefore, quality assessment for panoramic video attaches much importance in specifying video quality and improving related technologies. In this paper, a test plan for subjective quality assessment of panoramic videos is proposed, in which the test protocols needed during the subjective quality assessment are discussed in detail. With the proposed test plan, a subjective quality database is established for video coding applications. Statistical analysis indicates that the database shows a good distribution on the quality range, and thus proves the effectiveness of the proposed test plan, which can facilitate future studies in quality assessment.
In this paper, we compare the Double-Stimulus Impairment Scale (DSIS) and a Modified Absolute Category Rating (M-ACR) subjective quality evaluation method for HEVC/H.265-encoded omnidirectional videos. These two methods differ in the type of rating scale and presentation of stimuli. Results of our test provide insight into the similarities and differences between these two subjective test methods. Also, we investigate whether the results obtained with these subjective test methods are content-dependent. We evaluated subjective quality on an Oculus Rift for two different resolutions (4K and FHD) and at five different bit-rates. Experimental results show that for 4K resolution, for the lower bit-rates at 1 and 2 MBit/s, M-ACR provides slightly higher MOS compared to DSIS. For 4, 8, 15 MBit/s, DSIS provides slightly higher MOS. While the correlation coefficient between these two methods is very high, M-ACR offers a higher statistical reliability than DSIS. We also compared simulator sickness scores and viewing behavior. Experimental results show that subjects are more prone to simulator sickness while evaluating 360° videos with the DSIS method.
In this study, we investigate a VR simulator of a forestry crane used for loading logs onto a truck, mainly looking at Quality of Experience (QoE) aspects that may be relevant for task completion, but also whether there are any discomfort related symptoms experienced during task execution. The QoE test has been designed to capture both the general subjective experience of using the simulator and to study task completion rate. Moreover, a specific focus has been to study the effects of latency on the subjective experience, with regards both to delays in the crane control interface as well as lag in the visual scene rendering in the head mounted display (HMD). Two larger formal subjective studies have been performed: one with the VR-system as it is and one where we have added controlled delay to the display update and to the joystick signals. The baseline study shows that most people are more or less happy with the VR-system and that it does not have strong effects on any symptoms as listed in the Simulator Sickness Questionnaire (SSQ). In the delay study we found significant effects on Comfort Quality and Immersion Quality for higher Display delay (30 ms), but very small impact of joystick delay. Furthermore, the Display delay had strong influence on the symptoms in the SSQ, and causing test subjects to decide not to continue with the complete experiments. We found that this was especially connected to the longer added Display delays (≥ 20 ms).
The logic of the Bonferroni correction for multiple tests, or family-wise error, is to set the criterion to reduce the expected number of erroneous false positives, or Type I errors, below 1. This is a very stringent criterion for false positives in cases where the test may be applied millions of times, and will necessarily introduce a large proportion of false negatives (missed positives, or Type II errors). A proposed solution to this problem is to adjust the criterion for False Discovery Rate (Benjamini & Hochberg, 1995), which allows the number of false positives to increase proportionally to the number of true positives, though remaining at a small proportion, dramatically reducing the number of false negatives. This approach may be conceptualized as working with a relaxed confidence level that any one test is a true rather than a false positive, bringing the criterion more into line with our societal assessment of the validity of statements in general, and even in science, as having less than 100% certainty. The analytic strategy to the assessment of statistical significance provides a more intuitive approach to the identification of sparse signals in large datasets than the standard Bonferroni approach to correction for multiple tests.
Particular motions are important to play sports with high performance. The particular motions are mastered by learning motions, and visual information is considered to be effective for understanding and learning motions. In recent years, HMD with VR has been introduced as a new tool for learning motions with visual information. An advantage of the HMD-based motion learning method is that it enables learners to switch their observation view. Here, this research investigates basic view characteristics of observing and reproducing particular dynamic motions, which would be necessary to develop some methods for switching observation view properly. An experiment was conducted in order to study the basic view characteristics. As for the observation view factor, we prepared two factor levels, one was the front mirror view, and the other the rear camera view. In the experiment, a subject recognized and reproduced some reference dynamic motions on real time with each of the two views. The experimental results revealed that the reproduction performance with the rear camera view was significantly better than that with the front mirror view in the case of the depth-directional motions, compared with the other case of the depth-uncorrelated motions. It should be noted that the difference in the motion reproduction may become crucial for learners in particular as the motion velocity increases. It is supposed that the observation with the front mirror view requires some mental transformation operation when the learners reproduce motions. In selecting the observation view, it is required to minimize the mental transformation operation. The requirement is expected to be satisfied with the rear camera view, provided that occlusions are not crucial for learners to observe reference motions.
High quality, 360 capture for Cinematic VR is a relatively new and rapidly evolving technology. The field demands very high quality, distortionfree 360 capture which is not possible with cameras that depend on fisheye lenses for capturing a 360 field of view. The Facebook Surround 360 Camera, one of the few "players" in this space, is an open-source license design that Facebook has released for anyone that chooses to build it from off-the-shelf components and generate 8K stereo output using open-source licensed rendering software. However, the components are expensive and the system itself is extremely demanding in terms of computer hardware and software. Because of this, there have been very few implementations of this design and virtually no real deployment in the field. We have implemented the system, based on Facebook's design, and have been testing and deploying it in various situations; even generating short video clips. We have discovered in our recent experience that high quality, 360 capture comes with its own set of new challenges. As an example, even the most fundamental tools of photography like "exposure" become difficult because one is always faced with ultra-high dynamic range scenes (one camera is pointing directly at the sun and the others may be pointing to a dark shadow). The conventional imaging pipeline is further complicated by the fact that the stitching software has different effects on various aspects of the calibration or pipeline optimization. Most of our focus to date has been on optimizing the imaging pipeline and improving the quality of the output for viewing in an Oculus Rift headset. We designed a controlled experiment to study 5 key parameters in the rendering pipeline – black level, neutral balance, color correction matrix (CCM), geometric calibration and vignetting. By varying all of these parameters in a combinatorial manner, we were able to assess the relative impact of these parameters on the perceived image quality of the output. Our results thus far indicate that the output image quality is greatly influenced by the black level of the individual cameras (the Facebook camera comprised of 17 cameras whose output need to be stitched to obtain a 360 view). Neutral balance is least sensitive. We are most confused about the results we obtain from accurately calculating and applying the CCM for each individual camera. We obtained improved results by using the average of the matrices for all cameras. Future work includes evaluating the effects of geometric calibration and vignetting on quality.
Allowing viewers to explore virtual reality in a head-mounted display with six degrees of freedom (6-DoF) greatly enhances the associated immersion and comfort. It makes the experience more compelling compared to a fixed-viewpoint 2-DoF rendering produced by conventional algorithms using data from a stationary camera rig. In this work, we use subjective testing to study the relative importance of, and the interaction between, motion parallax and binocular disparity as depth cues that shape the perception of 3D environments by human viewers. Additionally, we use the recorded head trajectories to estimate the distribution of the head movements of a sedentary viewer exploring a virtual environment with 6-DoF. Finally, we demonstrate a real-time virtual reality rendering system that uses a Stacked OmniStereo intermediary representation to provide a 6-DoF viewing experience by utilizing data from a stationary camera rig. We outline the challenges involved in developing such a system and discuss the limitations of our approach.
Camera arrays are used to acquire the 360° surround video data presented on 3D immersive displays. The design of these arrays involves a large number of decisions ranging from the placement and orientation of the cameras to the choice of lenses and sensors. We implemented an open-source software environment (iset360) to support engineers designing and evaluating camera arrays for virtual and augmented reality applications. The software uses physically based ray tracing to simulate a 3D virtual spectral scene and traces these rays through multi-element spherical lenses to calculate the irradiance at the imaging sensor. The software then simulates imaging sensors to predict the captured images. The sensor data can be processed to produce the stereo and monoscopic 360° panoramas commonly used in virtual reality applications. By simulating the entire capture pipeline, we can visualize how changes in the system components influence the system performance. We demonstrate the use of the software by simulating a variety of different camera rigs, including the Facebook Surround360, the GoPro Odyssey, the GoPro Omni, and the Samsung Gear 360.