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Methods: We describe a spatiotemporal image perception model with static and dynamic signal and noise configurations. The 3D spatiotemporal noise is decomposed into 2D spatial noise and time-dependent noise with motion. The noise in the temporal domain is categorized into time-invariant fixed pattern noise (FPN) and temporal noise that varies per display frame. Visual integration of the moving signal and noise emulates the spatiotemporal image perception of dynamic detection targets in a smooth-pursuit event. A target detection model is implemented to compute the detectability of both low-contrast and high-resolution signal-known-exactly/background-known-exactly (SKE/BKE) targets in various static and dynamic imaging configurations using a non-pre-whitening model observer with eye filter (NPWE). Results: Smooth pursuit of a moving target suppresses the high-frequency dynamic resolution and noise in the orientation tangential to the motion trajectory. For the dynamic signal and noise configuration, the reduction of both resolution and high-frequency noise results in similar target detectability compared to the reference static image perception. On the other hand, the visibility of a moving target with static FPN is enhanced due to noise aliasing. Visual integration for approximately 33 ms of time-variant temporal noise at 90 Hz display refresh rate reduces the effective noise compared to the FPN by temporal fusion of noise in neighboring display frames. Conclusion: Spatiotemporal integration of dynamic signal and noise can potentially affect image quality. Complete assessment of image quality in MXR devices needs to consider the contributions from 3D spatiotemporal characteristics. |
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