Flat panels are by far the most common type of television screen. There are reasons, however, to think that curved screens create a greater sense of immersion, reduce distracting reflections, and minimize some perceptual distortions that are commonplace with large televisions. To examine these possibilities, we calculated how screen curvature affects the field of view and the probability of seeing reflections of ambient lights. We find that screen curvature has a small beneficial effect on field of view and a large beneficial effect on the probability of seeing reflections. We also collected behavioral data to characterize perceptual distortions in various viewing configurations. We find that curved screens can in fact reduce problematic perceptual distortions on large screens, but that the benefit depends on the geometry of the projection on such screens.
Foveated rendering is a performance optimization based on the well-known degradation of peripheral visual acuity. It reduces computational costs by showing a high-quality image in the user's central (foveal) vision and a lower quality image in the periphery. Foveated rendering is a promising optimization for Virtual Reality (VR) graphics, and generally requires accurate and low-latency eye tracking to ensure correctness even when a user makes large, fast eye movements such as saccades. However, due to the phenomenon of saccadic omission, it is possible that these requirements may be relaxed. In this paper, we explore the effect of latency for foveated rendering in VR applications. We evaluated the detectability of visual artifacts for three techniques capable of generating foveated images and for three different radii of the high-quality foveal region. Our results show that larger foveal regions allow for more aggressive foveation, but this effect is more pronounced for temporally stable foveation techniques. Added eye tracking latency of 80-150 ms causes a significant reduction in acceptable amount of foveation, but a similar decrease in acceptable foveation was not found for shorter eye tracking latencies of 20-40 ms, suggesting that a total system latency of 50-70 ms could be tolerated.
With the development of increasingly sophisticated computer graphics, there is a continuous growth of the variety and originality of virtual characters used in movies and games. So far however, their design has mostly been led by the artist's preferences, not by perceptual studies. We wanted to investigate how effective non-player character design can be used to influence gameplay. Namely, in a first study, we sought to find rules how to use a character's facial features to elicit perceptions of cartain personlity traits, using previous findings for human face perception as a basis. In our second study, we then tested how perceived personality traits of a non-player character could influence a player's moral decisions in a video game. Interacting with a less aggressive looking character led to less aggressive behavior towards a non-present individual. These results provide us both with better understanding of the perception of abstract virtual characters, their employment in video games, as well as giving us some insights about the factors underlying aggressive behavior in video games.
Unlike their human counterparts, artificial agents such as robots and game characters may be deployed with a large variety of face and body configurations. Some have articulated bodies but lack facial features, and others may be talking heads ending at the neck. Generally, they have many fewer degrees of freedom than humans through which they must express themselves, and there will inevitably be a filtering effect when mapping human motion onto the agent. In this paper, we investigate filtering effects on three types of embodiments, a) an agent with a body but no facial features, b) an agent with a head only and c) an agent with a body and a face. We performed a full performance capture of a mime actor enacting short interactions varying the non-verbal expression along five dimensions (e.g. level of frustration and level of certainty) for each of the three embodiments. We performed a crowd sourced evaluation experiment comparing the video of the actor to the video of an animated robot for the different embodiments and dimensions. Our findings suggest that the face is especially important to pinpoint emotional reactions, but is also most volatile to filtering effects. The body motion on the other hand had more diverse interpretations, but tended to preserve the interpretation after mapping, and thus proved to be more resilient to filtering.
This study compares three popular modalities for analyzing perceived video quality; user ratings, eye tracking and EEG. We contrast these three modalities for a given video sequence to determine if there is a gap between what humans consciously see and what we implicitly perceive. Participants are shown a video sequence with different artifacts appearing at specific distances in their field of vision; near foveal, middle peripheral and far peripheral. Our results show distinct differences between what we saccade to (eye-tracking), how we consciously rate video quality and our neural responses (EEG data). Our findings indicate that the measurement of perceived quality depends on the specific modality used.
Film directors are masters at controlling what we look at when we watch a film. However, there have been few quantitative studies of how gaze responds to cinematographic conventions thought to influence attention. We have collected and are releasing a data set designed to help investigate eye movements in response to higher level features such as faces, dialogue, camera movements, image composition, and edits. The data set, which will be released to the community, includes gaze information for 21 viewers watching 15 clips from live action 2D films, which have been hand annotated for high level features. This work has implications for the media studies, display technology, immersive reality, and human cognition.
With the development of high dynamic range images and tone mapping operators comes a need for image quality evaluation of tone mapped images. However, because of the significant difference in dynamic range between high dynamic range images and tone mapped images, conventional image quality assessment algorithms that predict distortion based on the magnitude of intensity or normalized contrast are not suitable for this task. In this paper, we present a feature-based quality metric for tone mapped images, which predicts the perceived quality by measuring the distortion in important image features that affect quality judgment. Our metric utilizes multi-exposed virtual photographs taken from the original high dynamic range images to bridge the gap between dynamic ranges in image feature analysis. By combining measures for brightness distortion, visual saliency distortion, and detail distortion in light and dark areas, the metric measures the overall perceptual distortion and assigns a score to a tone mapped image. Experiments on a subject-rated database indicate that the proposed metric is more consistent with subjective evaluation results than alternative approaches.
Advances in machine learning have produced systems that attain human-level performance on certain visual tasks, e.g., object identification. Nonetheless, other tasks requiring visual expertise are unlikely to be entrusted to machines for some time, e.g., satellite and medical imagery analysis. We describe a human-machine cooperative approach to visual search, the aim of which is to outperform either human or machine acting alone. The traditional route to augmenting human performance with automatic classifiers is to draw boxes around regions of an image deemed likely to contain a target. Human experts typically reject this type of hard highlighting. We propose instead a soft highlighting technique in which the saliency of regions of the visual field is modulated in a graded fashion based on classifier confidence level. We report on experiments with both synthetic and natural images showing that soft highlighting achieves a performance synergy surpassing that attained by hard highlighting.
Geometric modifications of 3D digital models are commonplace for the purpose of efficient rendering or compact storage. Modifications imply visual distortions which are hard to measure numerically. They depend not only on the model itself but also on how the model is visualized. We hypothesize that the model's light environment and the way it reflects incoming light strongly influences perceived quality. Hence, we conduct a perceptual study demonstrating that the same modifications can be masked, or conversely highlighted, by different light-matter interactions. Additionally, we propose a new metric that predicts the perceived distortion of 3D modifications for a known interaction. It includes computation over the object's appearance, \ie the light emitted by its surface in any direction given a known incoming light. Despite its simplicity, this metric competes with sophisticated perceptual metrics in terms of correlation to subjective measurements
Comprehension of computer programs is daunting, due in part to clutter in the software developers visual environment and the need for frequent visual context changes. Previous research has shown that non-speech sound can be useful in understanding the run-time behavior of a program. We explore the viability and advantages of using sound to help understand the static structure of software. A novel concept for auditory display of program elements is described in which non-speech sounds indicate characteristics and relationships among a Java programs classes, interfaces, and methods. An empirical study employing this concept was used to evaluate twenty-four sighted software professionals and students performing maintenance-oriented tasks using a 2 x 2 crossover. Viability is strong for di erentiation and characterization of software entities, less so for identification. The results show no overall advantage of using sound in terms of task duration at a 5% level of significance. The results do, however, suggest that sonification can be advantageous under certain conditions. While the subjects reported enthusiasm for the idea of sonification, it was mitigated by lack of familiarity with the concept and the brittleness of the tool. Limitations of the present research include restriction to particular types of comprehension tasks, a single sound mapping, a single programming language, and limited training time, but the use of sound in program comprehension shows sucient promise for continued research.Him