ISSN 1000-1239 CN 11-1777/TP

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基于全方向预测与误差扩展的可逆数据隐藏

曾骁 陈真勇 陈明 熊璋   

  1. (北京航空航天大学计算机学院 北京 100191) (zengxiao29@gmail.com)
  • 出版日期: 2010-09-15

Reversible Data Hiding Using Full-Context Prediction and Error Expansion

Zeng Xiao, Chen Zhenyong, Chen Ming, and Xiong Zhang   

  1. (School of Computer Science and Engineering, Beihang University, Beijing 100191)
  • Online: 2010-09-15

摘要: 提出一种具有高容量低失真特点的可逆图像数据隐藏算法.通过扩展像素的预测误差值将数据嵌入图像宿主中,可在提取嵌入的数据后准确还原原始图像.与大部分基于预测误差扩展的算法不同的是,提出一种具有全方向上下文的预测器以提高预测精度.提出一种基于差值扩展的嵌入算法将数据嵌入预测误差中,并提出一种仅需少量附加数据的边界表策略以避免像素溢出问题.实验数据表明,提出的全方向预测器能有效提高预测精度,并且结合提出的扩展算法与边界表,其嵌入容量与宿主图像质量较已有的可逆数据隐藏算法都有所提高.

关键词: 可逆数据隐藏, 图像, 全方向预测, 预测误差扩展, 边界表

Abstract: Proposed in this paper is a data hiding scheme featuring high capacity and low distortion. It is a reversible data hiding scheme, in which secret data is embedded into the host image by expanding the prediction errors of pixels, and the original host image can be exactly recovered once the embedded data is extracted. In this paper, three strategies are proposed to improve the performance. Different from most existing predictors, wherein only a partial prediction context is available, a predictor with full context for every pixel is proposed to enhance the accuracy. An expansion methods derived from difference expansion is presented for embedding secret data into the prediction errors. And a new boundary map strategy is proposed to avoid the overflow problem, which only introduces a little overhead data. Experimental results demonstrate that the proposed full-context predictor has better accuracy than other predictors and works out a more centralized histogram; and with the proposed expansion method as well as boundary map, the proposed scheme can provide a larger embedding capacity and a better image quality than those reported in other state-of-the-art literature. In addition, the proposed scheme is able to adjust the payload to embed different amount of bits into one prediction error.

Key words: reversible data hiding, image, full-context prediction, prediction error expansion, boundary map