A Perceptually Tuned Model for Just Noticeable Distortion Estimation for Videos

Abstract

Modeling the human visual system (HVS) is a challenging task but is necessary since it plays a crucial role in various image and video processing applications. In this paper, we construct a more complete Just Noticeable Distortion (JND) model for videos, where the foveation and the motion suppression factors are accounted for, in addition to the luminance adaptation, the contrast masking and the temporal masking effects which are considered in some existing JND models. JND is the maximum possible error in the signal which is not perceptible to the human eyes. Foveation is another significant feature of the HVS. It is based on the fact that the perceived scene is sampled spatially in a nonuniform manner by the HVS. The point of fixation corresponding to the observer’s gaze in the image has the highest visual sensitivity and the sensitivity decreases as we move away from it. One of the key properties of the human eye, the motion suppression effect, exploits the role of contextual motion in a video. It is determined by the absolute and relative velocities of different regions in a video. Experimental results showed that the JND estimated by our model is consistent with the HVS. An accurate estimate of the visibility threshold can be used to increase the performance of many video and image processing applications like compression, objective perceptual quality metrics, watermarking and visual data enhancement and restoration.