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Reversible data hiding based on the local smoothness estimator and optional embedding strategy in four prediction modes

机译:基于局部平滑度估计器和可选嵌入策略的四种预测模式下的可逆数据隐藏

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摘要

Four new prediction modes are proposed in this paper, each of which is a three-step process for all to-be-embedded pixels (nearly three-fourths of all the pixels). By designing each mode reasonably, all to-be-embedded pixels can be predicted with high accuracy, and thus, the number of embeddable pixels can be increased largely. In each step, a local smoothness estimator is utilized to determine if one embeddable pixel is located in a smooth or complex region, which is defined as the variance of the total neighbors of this pixel. In fact, the correlation evaluated by using the total neighbors, instead of a part, can reflect the complexity of the region more accurately. In this paper, an optional embedding strategy is introduced so as to select a low-distortion reversible data hiding (RDH) method according to the desired embedding rate (ER). Specifically, when the required ER is low, difference expansion (DE) is used to process those pixels in smooth regions while leaving the rest unaltered. With ER largely increased, adaptive embedding is used to embed 2-bit into these pixels with low local variance by DE while 1-bit into the remaining ones. The experimental results also demonstrate the proposed method is effective.
机译:本文提出了四种新的预测模式,每种模式都是针对所有待嵌入像素(约占所有像素的四分之三)的三步过程。通过合理地设计每种模式,可以高精度地预测所有将要嵌入的像素,因此,可以大大增加可嵌入像素的数量。在每一步骤中,利用局部平滑度估计器来确定一个可嵌入像素是否位于平滑或复杂区域中,该区域被定义为该像素的全部邻居的方差。实际上,使用总邻居而不是一部分评估的相关性可以更准确地反映区域的复杂性。本文介绍了一种可选的嵌入策略,以便根据所需的嵌入率(ER)选择低失真可逆数据隐藏(RDH)方法。具体而言,当所需的ER低时,使用差异扩展(DE)来处理平滑区域中的那些像素,而其余部分保持不变。随着ER的大幅增加,自适应嵌入用于将2位嵌入到这些像素中,而DE的局部方差很小,而1位嵌入到其余像素中。实验结果也证明了该方法的有效性。

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