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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...
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Citations
... Chen et al. [25] constructed an asymmetric foveated JND model using the effect of eccentricity on visual sensitivity. Wang et al. [26] proposed a superpixel-wise JND model based on region modulation. Jiang et al. [27] took a different approach and proposed a JND model based on top-down design philosophy. ...
The just noticeable difference (JND) model reflects the visibility limitations of the human visual system (HVS), which plays an important role in perceptual image/video processing and is commonly applied to perceptual redundancy removal. However, existing JND models are usually constructed by treating the color components of three channels equally, and their estimation of the masking effect is inadequate. In this paper, we introduce visual saliency and color sensitivity modulation to improve the JND model. Firstly, we comprehensively combined contrast masking, pattern masking, and edge protection to estimate the masking effect. Then, the visual saliency of HVS was taken into account to adaptively modulate the masking effect. Finally, we built color sensitivity modulation according to the perceptual sensitivities of HVS, to adjust the sub-JND thresholds of Y, Cb, and Cr components. Thus, the color-sensitivity-based JND model (CSJND) was constructed. Extensive experiments and subjective tests were conducted to verify the effectiveness of the CSJND model. We found that consistency between the CSJND model and HVS was better than existing state-of-the-art JND models.
