Adaptive reversible data hiding through autoregression
Adaptive reversible data hiding through autoregression
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DOI：
10.1109/icist.2012.6221765
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Jingyang Wen;Jinli Lei;Y. Wan
中科院分区：
文献类型：
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作者：
Jingyang Wen;Jinli Lei;Y. Wan
提出了一种基于自回归的自适应可逆数据隐藏方法。在该算法中，我们重点研究了图像像素值的预测，它在数据嵌入过程中起着关键作用。与传统的数据隐藏技术不同，该算法对每幅图像调整阈值，将所有像素分为两个区域：平滑区域和纹理区域。然后，该算法通过最小二乘最小化最优估计像素值预测的自回归模型的系数。预测误差被自适应地最小化以实现高预测精度，从而利用图像中的更多冗余来实现非常高的数据嵌入容量，同时保持低失真。实验结果表明，该算法优于典型的国家的最先进的方法在一般。
An adaptive reversible data hiding method through autoregression is presented in this paper. In the proposed algorithm, we focus on the image pixel value prediction, which plays a key role in the data embedding process. Unlike conventional data hiding techniques, a threshold is adjusted for each image to divide all pixels into two regions: the smooth region and the texture region. Then the proposed algorithm optimally estimates the coefficients of autoregression model for pixel value prediction through least-squares minimization. The prediction error is adaptively minimized to achieve high prediction accuracy so that more redundancy in the image is exploited to achieve very high data embedding capacity while keeping the distortion low. Experimental results show that the proposed algorithm outperforms typical state-of-the-art methods in general.