Mitigating Cybersickness in Virtual Reality Systems through Foveated Depth-of-Field Blur

Cybersickness is one of the major roadblocks in the widespread adoption of mixed reality devices. Prolonged exposure to these devices, especially virtual reality devices, can cause users to feel discomfort and nausea, spoiling the immersive experience. Incorporating spatial blur in stereoscopic 3D stimuli has shown to reduce cybersickness. In this paper, we develop a technique to incorporate spatial blur in VR systems inspired by the human physiological system. The technique makes use of concepts from foveated imaging and depth-of-field. The developed technique can be applied to any eye tracker equipped VR system as a post-processing step to provide an artifact-free scene. We verify the usefulness of the proposed system by conducting a user study on cybersickness evaluation. We used a custom-built rollercoaster VR environment developed in Unity and an HTC Vive Pro Eye headset to interact with the user. A Simulator Sickness Questionnaire was used to measure the induced sickness while gaze and heart rate data were recorded for quantitative analysis. The experimental analysis highlighted the aptness of our foveated depth-of-field effect in reducing cybersickness in virtual environments by reducing the sickness scores by approximately 66%.

Double Ring Model for Foveated Imaging

Human visual system has a space-variant resolution nature. In the retinal receptive field, the resolution is not uniform but sampled finest in the central fovea and coarser in the peripheral. This variable resolution mapping function is born by the cerebral primary visual cortex V1. It has a clear visual field map of spatial information, and this spatial mapping structure is called Retinotopy. The forward mapping to visual cortex from retina is characterized with complex LPT (Log-PolarTransform) by Schwartz. The retinal receptive field image is reconstructed by inverse projection LPT-1 from V1. This reconstructed process is called F oveated I maging. Since the spatial information is concentrated in the center of the visual field, the Foveated Imaging is applied to image compression, pattern recognition, robot vision, and/or computer vision. The retinal receptive field image is suitable for material appearance expression with natural blurring due to peripheral vision.<br/> However, the complexity of the inverse transform LPT-1 was a bottleneck. This paper proposes a Double- Ring-structured novel Foveated Imaging method using positive and negative Gaussian blur masks without using the inverse transform LPT-1 of Schwartz theory and reports the evaluation of reproduction errors.

Foveation Pipeline for 360° Video-Based Telemedicine

Pan-tilt-zoom (PTZ) and omnidirectional cameras serve as a video-mediated communication interface for telemedicine. Most cases use either PTZ or omnidirectional cameras exclusively; even when used together, images from the two are shown separately on 2D displays. Conventional foveated imaging techniques may offer a solution for exploiting the benefits of both cameras, i.e., the high resolution of the PTZ camera and the wide field-of-view of the omnidirectional camera, but displaying the unified image on a 2D display would reduce the benefit of “omni-” directionality. In this paper, we introduce a foveated imaging pipeline designed to support virtual reality head-mounted displays (HMDs). The pipeline consists of two parallel processes: one for estimating parameters for the integration of the two images and another for rendering images in real time. A control mechanism for placing the foveal region (i.e., high-resolution area) in the scene and zooming is also proposed. Our evaluations showed that the proposed pipeline achieved, on average, 17 frames per second when rendering the foveated view on an HMD, and showed angular resolution improvement on the foveal region compared with the omnidirectional camera view. However, the improvement was less significant when the zoom level was 8× and more. We discuss possible improvement points and future research directions.

A Gaze-Contingent System for Foveated Multiresolution Visualization of Vector and Volumetric Data

Computational complexity is a limiting factor for visualizing large-scale scientific data. Most approaches to render large datasets are focused on novel algorithms that leverage cutting-edge graphics hardware to provide users with an interactive experience. In this paper, we alternatively demonstrate foveated imaging which allows interactive exploration using low-cost hardware by tracking the gaze of a participant to drive the rendering quality of an image. Foveated imaging exploits the fact that the spatial resolution of the human visual system decreases dramatically away from the central point of gaze, allowing computational resources to be reserved for areas of importance. We demonstrate this approach using face tracking to identify the gaze point of the participant for both vector and volumetric datasets and evaluate our results by comparing against traditional techniques. In our evaluation, we found a significant increase in computational performance using our foveated imaging approach while maintaining high image quality in regions of visual attention.

A Perceptually Optimized Foveation Wavelet Visible Difference Predictor Quality Metric Based on Psychovisual Properties of the Human Visual System (HVS)

Region of interest (ROI) image and video compression techniques have been widely used in visual communication applications in an effort to deliver good quality images and videos at limited bandwidths. Foveated imaging exploits the fact that the spatial resolution of the human visual system (HVS) is highest around the point of fixation (foveation point) and decreases dramatically with increasing eccentricity. Exploiting this fact, the authors have developed an appropriate metric for the assessment of ROI coded images, adapted to foveation image coding based on psycho-visual quality optimization tools, which objectively enable us to assess the visual quality measurement with respect to the region of interest (ROI) of the human observer. The proposed metric yields a quality factor called foveation probability score (FPS) that correlates well with visual error perception and demonstrating very good perceptual quality evaluation.