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A DCT-domain JND model based on visual attention for image

机译:基于图像视觉关注的DCT域JND模型

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Most of the traditional JND models in DCT domain compute the JND threshold by incorporating the spatial contrast sensitivity function, the luminance adaptation effect and the contrast masking effect. How to integrate visual attention effect into the traditional JND models is still an open problem. In this paper, we proposed a new DCT-domain JND profile, in which a combined modulation function is built, based on the image saliency and textural characteristic to describe the visual attention effect and contrast masking effect on JND Threshold in DCT domain. Experimental results show that the proposed model can tolerate more distortion with the same perceptual quality, compared with the latest DCT-domain JND model. In terms of PSNR, the improvement of tolerated distortion is 0.54dB on average.
机译:DCT域中的大多数传统JND模型通过结合空间对比度灵敏度,亮度适应效果和对比度屏蔽效果来计算JND阈值。如何将可视注意效果集成到传统的JND模型中仍然是一个开放的问题。在本文中,我们提出了一种新的DCT-域JND配置文件,其中基于图像显着性和纹理特性构建了组合的调制功能,以描述DCT域中的JND阈值对jND阈值的视觉注意力效果和对比掩蔽效应。实验结果表明,与最新的DCT域JND模型相比,所提出的模型可以容忍与相同的感知质量相同的变形。就PSNR而言,平均耐受性失真的提高为0.54dB。

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