Judder Modelling Framework with Perceptual Quality Score Prediction for HDR Videos

Judder is a motion artifact describing a perceptual mismatch between human visual system and discrete movements on a display. Judder is correlated with video frame rates, motion speeds and brightness. It significantly degrades the perceived video quality. We present a framework for modeling judder, where the input frames are processed to generate optical flow and a sensitivity map. Concurrently, a specifically designed attention map is integrated into the process. These components collectively contribute to the creation of a feature map, which is utilized to compute judder scores. The proposed approach explicitly takes into account the video frame rate, resulting in an accurate judder score to predict the subjective mean opinion scores ultimately. Our assessment of the proposed framework on an HDR video dataset shows that judderness is highly influenced by the frame rate and, to some extent, by motion speed and brightness. The prediction of perceived quality scores shows improvement compared to the baseline framework.