Spatiotemporal visualization of image registration on augmented reality head-mounted displays

Real-time image registration and visualization with augmented reality (AR) head-mounted displays (HMDs) are computationally and rendering intensive. Limited computational resources create trade-offs between rendering image resolution, latency, and geometric accuracy. Spatiotemporal image artifacts such as motion blur, multiple images, and flicker span in both spatial and temporal domains when a target or the HMD is in motion. It remains challenging to evaluate the spatiotemporal image quality using conventional methods. In this study, we performed both theoretical simulations and empirical measurements to evaluate the spatiotemporal image visualization on AR image registration. We developed a spatiotemporal visual performance metric combining system imaging characteristics with human visual capabilities. Spatiotemporal image quality was evaluated using various motion and HMD hardware configurations.