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首页> 外文会议>International Conference on Cloud Computing and Security >A Multiple Linear Regression Based High-Performance Error Prediction Method for Reversible Data Hiding
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A Multiple Linear Regression Based High-Performance Error Prediction Method for Reversible Data Hiding

机译：基于多元的回归基于线性回归的可逆数据隐藏的高性能误差预测方法

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摘要

In this paper, a high-performance error-prediction method based on Multiple Linear Regression (MLR) algorithm is first proposed to improve the performance of Reversible Data Hiding (RDH). The MLR matrix function that indicates the inner correlations between the pixels and its neighbors is established adaptively according to the consistency of pixels in local area of a natural image, and thus the object pixel is predicted accurately with the achieved MLR function that satisfies the consistency of the neighboring pixels. Compared with conventional methods that only predict the object pixel with fixed parameters predictors through simple arithmetic combination of its surroundings pixel, experimental results show that the proposed method can provide a sparser prediction-error image for data embedding, and thus improves the performance of RDH more effectively than those state-of-the-art error prediction algorithms.
机译：本文首先提出了一种基于多元线性回归（MLR）算法的高性能误差预测方法，提高可逆数据隐藏（RDH）的性能。指示像素和其邻居之间的内部相关性的MLR矩阵函数根据自然图像的局部区域中的像素的一致性自适应地建立，因此可以使用实现的MLR函数精确地预测对象像素，该MLR函数满足符合的邻居像素。与通过其周围环境像素的简单算术组合预测具有固定参数预测器的对象像素的传统方法相比，实验结果表明该方法可以为数据嵌入提供稀疏预测误差图像，从而提高RDH的性能优于那些最先进的误差预测算法。

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