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首页> 外文期刊>Journal of visual communication & image representation >Locally optimum image watermark decoder by modeling NSCT domain difference coefficients with vector based Cauchy distribution
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Locally optimum image watermark decoder by modeling NSCT domain difference coefficients with vector based Cauchy distribution

机译：基于向量的Cauchy分布模拟NSCT域差系数的本地最佳图像水印解码器

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摘要

Improving the ability of imperceptibility, watermark capacity, and robustness at the same time still remains a challenge within the digital image watermarking community. By modeling the robust nonsub-sampled Contourlet transform (NSCT) difference coefficients with vector based Cauchy distribution and employing locally most powerful (LMP) test, we propose a locally optimum image watermark decoder in NSCT domain. We first compute the difference coefficients according to the inter-scale dependency between NSCT coefficients, and investigate the robustness of the NSCT difference coefficients by subjective visual error and objective mean squared error (MSE) terms. We then embed the digital watermark into the significant NSCT difference subband with highest energy by modifying the robust NSCT difference coefficients. At the receiver, by combining the vector based Cauchy probability distribution and LMP test, we propose a locally optimum blind watermark decoder in the NSCT domain. Here, robust NSCT difference coefficients are firstly modeled by employing the vector based Cauchy probability density function (PDF), where the Cauchy marginal statistics and various strong dependencies of NSCT coefficients are incorporated. Then the statistical model parameters of vector based Cauchy PDF are estimated using second-kind statistics approach. And finally a blind image watermark decoder is developed using vector based Cauchy PDF and LMP decision rule. We conduct extensive experiments to evaluate the performance of the proposed blind watermark decoder, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature. (C) 2019 Elsevier Inc. All rights reserved.
机译：同时提高难以察觉，水印容量和鲁棒性的能力仍然是数字图像水印社区内的挑战。通过使用向量的Cauchy分布和采用本地最强大的（LMP）测试的鲁棒非鲁布采样的Contourlet变换（NSCT）差系数，我们提出了NSCT域中的局部最佳图像水印解码器。我们首先根据NSCT系数之间的刻度依赖性计算差系数，并通过主观视觉误差和目标平均方形错误（MSE）术语来研究NSCT差系数的鲁棒性。然后，我们通过修改鲁棒NSCT差系数，将数字水印与最高能量嵌入到具有最高能量的显着的NSCT差子带中。在接收器中，通过组合基于矢量的Cauchy概率分布和LMP测试，我们提出了NSCT域中的局部最佳盲水印解码器。这里，通过采用基于向量的Cauchy概率密度（PDF）来首先建模鲁棒NSCT差系数，其中Cauchy边缘统计和NSCT系数的各种强依赖性被纳入。然后使用二种统计方法估计向量基Cauchy PDF的统计模型参数。最后使用基于矢量的Cauchy PDF和LMP决策规则开发了盲目图像水印解码器。我们对评估拟议的盲目水印解码器的性能进行了广泛的实验，其中令人鼓舞的结果验证了拟议技术的有效性，与最近在文献中提出的最新方法相比。 （c）2019 Elsevier Inc.保留所有权利。

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