Feedforward and Feedback Modulations Based Foveated JND Estimation for Images

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