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论文摘要

基于差值直方图平移的彩色图像可逆信息隐藏

Reversible Information Hiding for Color Images Based on Difference Histogram Shifting

作者:熊志勇(中南民族大学 计算机科学学院);王江晴(中南民族大学 计算机科学学院)

Author:Xiong Zhiyong(College of Computer Sci.,SouthCentral Univ. for Nationalities);Wang Jiangqing(College of Computer Sci.,SouthCentral Univ. for Nationalities)

收稿日期:2010-05-08          年卷(期)页码:2011,43(3):81-89

期刊名称:工程科学与技术

Journal Name:Advanced Engineering Sciences

关键字:可逆信息隐藏;差值直方图平移;预测误差差值;直方图平移;像素值可扩展性

Key words:reversible data hiding;difference histogram shifting;prediction error difference;histogram shifting;expandability of pixels value

基金项目:国家自然科学基金资助项目(60975021);中央高校基本科研业务费专项资金资助项目(CZZ11003)

中文摘要

针对现有直方图平移算法嵌入容量偏低、不适合彩色图像等缺点,提出一种基于预测误差差值直方图平移的彩色图像可逆信息隐藏算法。除使用双分量差值以外,还加入了第3分量计算差值,根据预测误差之间的关系,将差值分成5种,从而有效地减小差值,提高嵌入容量和图像质量。通过扩展像素值嵌入信息,利用像素值可扩展性定位不可扩展像素,并嵌入少量的标志信息代替溢出定位图,采用两轮嵌入避免使用最坏测试法判断像素值可扩展性,以减少辅助信息量,进一步提高嵌入容量。实验结果表明,本文算法在嵌入容量和图像质量两方面均有较大优势,与其它可逆嵌入方法相比,算法整体性能更高。

英文摘要

To avoid the drawback of histogram shifting algorithm, which has lower embedding capacity and is not suitable for color images, a reversible information hiding algorithm for color images based on prediction error difference histogram shifting was proposed. In addition to the difference between random two components, the algorithm also added the third component to calculate difference value, according to the relationship between prediction errors, the difference was divided into five kinds, so as to effectively reduce the difference value to improve the embedding capacity and stego-image quality. The embedding scheme expanded pixels value to embed information, and utilized the expandability of pixels value to locate pixels which can not be expanded, and embed a few flag information instead of overflow location map, with two steps embedding method, avoid using worst test method to determine expandability of pixels value, therefore the quantity of auxiliary information was decreased. Experimental results showed the embedding capacity and the quality of stego-image are significantly improved, when compared with other new or classical reversible embedding algorithms.

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