AR-DAVID: Augmented Reality Display Artifact Video Dataset

Abstract

The perception of visual content in optical-see-through augmented reality (AR) devices is affected by the light coming from the environment. This additional light interacts with the content in a non-trivial manner because of the illusion of transparency, different focal depths, and motion parallax. To investigate the impact of environment light on display artifact visibility (such as blur or color fringes), we created the first subjective quality dataset targeted toward augmented reality displays. Our study consisted of 6 scenes, each affected by one of 6 distortions at two strength levels, seen against one of 3 background patterns shown at 2 luminance levels: 432 conditions in total. Our dataset shows that environment light has a much smaller masking effect than expected. Further, we show that this effect cannot be explained by compositing of the AR-content with the background using optical blending models. As a consequence, we demonstrate that existing video quality metrics perform worse than expected when predicting the perceived magnitude of degradation in AR displays, motivating further research.