Perceptual Flicker Visibility Prediction Model

Abstract

The mere presence of spatiotemporal distortions in digital videos does not have to imply quality degradation since distortion visibility can be strongly reduced by the perceptual phenomenon of visual masking. Flicker is a particularly annoying occurrence, which can arise from a variety of distortion processes. Yet flicker can also be suppressed by masking. We propose a perceptual flicker visibility prediction model which is based on a recently discovered visual change silencing phenomenon. The proposed model predicts flicker visibility on both static and moving regions without any need for content-dependent thresholds. Using a simple model of cortical responses to video flicker, an energy model of motion perception, and a divisive normalization stage, the system captures the local spectral signatures of flicker distortions and predicts perceptual flicker visibility. The model not only predicts silenced flicker distortions in the presence of motion, but also provides a pixel-wise flicker visibility index. Results show that the predicted flicker visibility model correlates well with human percepts of flicker distortions tested on the LIVE Flicker Video Database and is highly competitive with current flicker visibility prediction methods.