A Superpixel-Wise Just Noticeable Distortion Model

Abstract

The just noticeable distortion (JND) model reveals the visibility limitation. Human eyes hold different attention and sensitivity to different regions owing to different contributions to the perceptual quality. In this paper, a superpixel-wise JND model based on region (RJND) is proposed. First, an image is segmented into superpixels by the simple linear iterative clustering (SLIC). Then, region color contrast is calculated for each region and foveation regions are selected for the image. Based on the human visual perception, a region weighting model is established by incorporating region color contrast and foveation regions modulation. Considering the contrast masking (CM) effect is not perfect, we introduce the texture coarseness combined with CM effect for a more accurate visual masking effect. Finally, a new region JND model is established by combining the region weighting model and the coarseness modulation. The experimental results demonstrate the proposed RJND model can decrease PSNR more efficiently compared with some existing JND models when achieving nearly the same subjective perceptual quality. It can remove much more visual redundancy.

Keywords

• Just noticeable distortion
• superpixel
• texture coarseness
• color contrast
• foveation region