Aliasing effects due to time-discrete capturing of amplitude-modulated light with a digital image sensor are perceived as flicker by humans. Especially when observing these artifacts in digital mirror replacement systems, they are annoying and can pose a risk. Therefore, ISO 16505 requires flicker-free reproduction for 90% of people in these systems. Various psychophysical studies investigate the influence of large-area flickering of displays, environmental light, or flickering in television applications on perception and concentration. However, no detailed knowledge of subjective annoyance/irritation due to flicker from camera-monitor systems as a mirror replacement in vehicles exist so far, but the number of these systems is constantly increasing. This psychophysical study used a novel data set from real-world driving scenes and synthetic simulation with synthetic flicker. More than 25 test persons were asked to quantify the subjective annoyance level of different flicker frequencies, amplitudes, mean values, sizes, and positions. The results show that for digital mirror replacement systems, human subjective annoyance due to flicker is greatest in the 15 Hz range with increasing amplitude and magnitude. Additionally, the sensitivity to flicker artifacts increases with the duration of observation.
Novel display algorithms such as low-persistence displays, black frame insertion, and temporal resolution multiplexing introduce temporal change into images at 40-180 Hz, on the boundary of the temporal integration of the visual system. This can lead to flicker, a highly-objectionable artifact known to induce viewer discomfort. The critical flicker frequency (CFF) alone does not model this phenomenon well, as flicker sensitivity varies with contrast, and spatial frequency; a content-aware model is required. In this paper, we introduce a visual model for predicting flicker visibility in temporally changing images. The model performs a multi-scale analysis on the difference between consecutive frames, normalizing values with the spatio-temporal contrast sensitivity function as approximated by the pyramid of visibility. The output of the model is a 2D detection probability map. We ran a subjective flicker marking experiment to fit the model parameters, then analyze the difference between two display algorithms, black frame insertion and temporal resolution multiplexing, to demonstrate the application of our model.