A Novel Foveated-JND Profile Based on an Adaptive Foveated Weighting Model

The aftereffect of visual attention (VA) and foveated masking (FM) are two important HVS characteristics not fully exploited in conventional foveated JND (FJND) models. This paper presents a fixation point estimation method to build adaptive foveated weighting model, by which an improved FJND profile is developed with VA and FM effects considered. The proposed FJND profile overcomes two limitations of the conventional FJND profiles. On one hand, conventional FJND profiles can’t get accurate positions of fixation points. This paper proposes a fixation prediction algorithm based on the distribution of cones to identify fixation points and regions. On the other hand, conventional FJND profiles did not incorporate the VA effect. By fully exploiting the VA and FM effects, this work proposes an adaptive foveated weighting model, which is developed as a function of the fixation intensity and retinal eccentricity. Here, the fixation intensity is estimated from the saliency map which is modelled by the Gaussian Mixed Model. With the proposed weight model, a new FJND profile is developed. Experimental results show that the proposed profile tolerates more distortion at the same perceptual image quality compared with other JND profiles.