As virtual reality (VR) headsets continue to achieve ever more immersive visuals along the axes of resolution, field of view, focal cues, distortion mitigation, and so on, the luminance and dynamic range of these devices falls far short of widely available consumer televisions. While work remains to be done on the display architecture side, power and weight limitations in head-mounted displays pose a challenge for designs aiming for high luminance. In this paper, we seek to gain a basic understanding of VR user preferences for display luminance values in relation to known, real-world luminances for immersive, natural scenes. To do so, we analyze the luminance characteristics of an existing high-dynamic-range (HDR) panoramic image dataset, build an HDR VR headset capable of reproducing over 20,000 nits peak luminance, and conduct a first-of-its-kind study on user brightness preferences in VR. We conclude that current commercial VR headsets do not meet user preferences for display luminance, even for indoor scenes.

FovVideoVDP is a video difference metric that models the spatial, temporal, and peripheral aspects of perception. While many other metrics are available, our work provides the first practical treatment of these three central aspects of vision simultaneously. The complex interplay between spatial and temporal sensitivity across retinal locations is especially important for displays that cover a large field-of-view, such as Virtual and Augmented Reality displays, and associated methods, such as foveated rendering. Our metric is derived from psychophysical studies of the early visual system, which model spatio-temporal contrast sensitivity, cortical magnification and contrast masking. It accounts for physical specification of the display (luminance, size, resolution) and viewing distance. To validate the metric, we collected a novel foveated rendering dataset which captures quality degradation due to sampling and reconstruction. To demonstrate our algorithm's generality, we test it on 3 independent foveated video datasets, and on a large image quality dataset, achieving the best performance across all datasets when compared to the state-of-the-art.