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.
Highlights
It is known that the visual sensitivity of human visual system (HVS) is limited
In order to prove the key contribution of region weighting modulation and texture coarseness modulation to the performance improvement, the experiments are carried out through control variables
There are four images processed by different just noticeable distortion (JND) models, including original model, model only based on coarseness modulation, model only based on region weighting modulation and the proposed model
Summary
It is known that the visual sensitivity of human visual system (HVS) is limited. Human eyes cannot perceive content changes when pixel differences are lower than a certain threshold. In [6], Wu et al proposed the effect of disorderly concealment to estimate the JND thresholds. Wang et al [12] proposed a novel adaptive foveated weighting JND model considering both the foveated masking effect and the visual attention effect. The paper proposes the superpixel-wise JND model based on regions (RJND). We propose the texture coarseness modulation factor to estimate texture feature of each region and improve the masking effect. According to the HVS, we select the foveation regions and establish the foveation modulation factor to estimate the visual importance of each region based on the color contrast. We propose the texture coarseness modulation factor according to different noise masking abilities of texture regions, which is used to estimate the more accurate visual masking effect.
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