Perceptual Visibility Model for Temporal Contrast Changes in Periphery

TL;DR

This paper develops a perceptual model for understanding how humans perceive temporal changes in the peripheral vision, especially relevant for wide-field displays like VR and AR, enabling improved content rendering and attention modeling.

Contribution

It introduces new psychophysical experiments and a perceptual model specifically for peripheral temporal perception, extending beyond foveal-focused models.

Findings

Model accurately predicts peripheral temporal sensitivity.

Enables seamless content injection in the periphery.

Assists in optimizing foveated rendering to reduce aliasing.

Abstract

Modeling perception is critical for many applications and developments in computer graphics to optimize and evaluate content generation techniques. Most of the work to date has focused on central (foveal) vision. However, this is insufficient for novel wide-field-of-view display devices, such as virtual and augmented reality headsets. Furthermore, the perceptual models proposed for the fovea do not readily extend to the off-center, peripheral visual field, where human perception is drastically different. In this paper, we focus on modeling the temporal aspect of visual perception in the periphery. We present new psychophysical experiments that measure the sensitivity of human observers to different spatio-temporal stimuli across a wide field of view. We use the collected data to build a perceptual model for the visibility of temporal changes at different eccentricities in complex video content. Finally, we discuss, demonstrate, and evaluate several problems that can be addressed using our technique. First, we show how our model enables injecting new content into the periphery without distracting the viewer, and we discuss the link between the model and human attention. Second, we demonstrate how foveated rendering methods can be evaluated and optimized to limit the visibility of temporal aliasing.