Feedforward and Feedback Modulations Based Foveated JND Estimation for Images

Abstract

The just noticeable difference (JND) reveals the key characteristic of visual perception, which has been widely used in many perception-based image and video applications. Nevertheless, the modulatory mechanism of the human visual system (HVS) has not been fully exploited in JND threshold estimation, which results in the existing JND models not being accurate enough. In this article, by analyzing the feedforward and feedback modulatory behaviors in the HVS, an enhanced foveated JND (FJND) estimation model is proposed considering modulatory effects and masking effects in visual perception. The contributions of this article are mainly twofold. On the one hand, by analyzing the modulatory behaviors in the HVS, the modulatory mechanism is incorporated into JND estimation and a hierarchical modulation-based JND estimation framework is proposed for the first time. On the other hand, according to the response characteristics of visual neurons, modulatory effects on visual sensitivity are formulated as several modulatory factors to modulate the estimated JND threshold properly. Compared with existing models, the proposed model is developed in view of not only the masking effects but also the modulatory effects, which makes our model more consistent with the HVS. For different complex input images, experimental results show that the proposed FJND model tolerates more distortion at the same perceptual quality in comparison with other existing models.