A JND Profile Based on Hierarchically Selective Attention for Images

Abstract

Most of the traditional just-noticeable-distortion (JND) models in pixel domain compute the JND threshold by incorporating the spatial luminance adaptation effect and the textures contrast masking effect. Recently, with the rapid development of the computable models of visual attention, researchers started to improve the JND model by considering visual saliency of images, a foveated spatial JND model (FSJND) was proposed by incorporating the traditional visual characteristics and fovea characteristic of human eyes to enhance JND thresholds. However, the thresholds computed by the FSJND model may be overestimated for some high resolution images. In this paper, we proposed a new JND profile in pixel domain, in which a multi-level modulation function is built to reflect the effect of hierarchically selective visual attention on JND thresholds. The contrast masking is also considered in our modulation function to obtain more accurate JND thresholds. Compared with the lasted JND profiles, the proposed model can tolerate more distortion and has much better perceptual quality. The proposed JND model can be easily applied in many areas, such as compression, error protection, and so on.