Visual Attention Guided Pixel-Wise Just Noticeable Difference Model
Visual Attention Guided Pixel-Wise Just Noticeable Difference Model
复制标题
视觉注意引导的逐像素可觉差异模型
DOI：
10.1109/access.2019.2939569
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发表时间：
2019-09
期刊：
影响因子：
3.9
通讯作者：
Ma Kai-Kuang
中科院分区：
文献类型：
--
作者：
Zeng Zhipeng;Zeng Huanqiang;Chen Jing;Zhu Jianqing;Zhang Yun;Ma Kai-Kuang
像素域的JND模型一般由亮度自适应(LA)和对比度掩蔽(CM)组成，其中考虑了边缘掩蔽(EM)和纹理掩蔽(TM)。然而，在现有的像素级JND模型中，由于高估了规则方向纹理区域的掩蔽效果，而忽略了人眼对真实图像的视觉注意特性，因此没有对CM进行适当的评估。本文提出了一种新的像素域JND模型，其中基于方向复杂性，提出了规则纹理区域(也称为有序纹理区域)的有序纹理掩蔽(OTM)和复杂纹理区域(也称为无序纹理区域)的无序纹理掩蔽(DTM)。同时，将视觉显著度作为权重因子，并将其引入到CM评价中，以提高JND阈值。实验结果表明，与已有的相关JND模型相比，JND模型在相同的感知质量下容忍了更多的失真，并在相同注入的JND噪声能量水平上带来了更好的视觉感知。
The just noticeable difference (JND) models in pixel domain are generally composed of luminance adaptation (LA) and contrast masking (CM), which takes edge masking (EM) and texture masking (TM) into consideration. However, in existing pixel-wise JND models, CM is not evaluated appropriately since they overestimate the masking effect of regular oriented texture regions and neglect the visual attention characteristic of human eyes for the real image. In this work, a novel JND model in pixel domain is proposed, where orderly texture masking (OTM) for regular texture areas (also called orderly texture regions) and disorderly texture masking (DTM) for complex texture areas (also called disorderly texture regions) are presented based on the orientation complexity. Meanwhile, the visual saliency is set as the weighting factor and is incorporated into CM evaluation to enhance JND thresholds. Experimental results indicate that compared with existing relevant JND profiles, the proposed JND model tolerates more distortion in the same perceptual quality, and brings better visual perception in the same level of the injected JND-noise energy.
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