应用科学学报 ›› 2018, Vol. 36 ›› Issue (2): 362-370.doi: 10.3969/j.issn.0255-8297.2018.02.014

• 多媒体信息安全专刊 • 上一篇    下一篇

基于多元线性回归的自适应图像可逆信息隐藏误差预测算法

王晓雨1, 马宾1, 李健2, 施云庆3   

  1. 1. 齐鲁工业大学 信息学院, 济南 250300;
    2. 南京信息工程大学 计算机与软件学院, 南京 210044;
    3. 新泽西理工大学 电子与计算机工程系, 新泽西 07102, 美国
  • 收稿日期:2018-02-11 出版日期:2018-03-31 发布日期:2018-03-31
  • 通信作者: 马宾,教授,研究方向:可逆信息隐藏,E-mail:sddxmb@126.com E-mail:sddxmb@126.com
  • 基金资助:

    国家自然科学基金(No.41202206);山东省自然科学基金(No.ZR2012F014);济南市高校院所自主创新计划基金(No.JN201402005)资助

Adaptive Image Reversible Data Hiding Error Prediction Algorithm Based on Multiple Linear Regression

WANG Xiao-yu1, MA Bin1, LI Jian2, SHI Yun-qing3   

  1. 1. School of Information Science, Qilu University of Technology, Jinan 250300, China;
    2. School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China;
    3. Electrical and Computer Engineering, New Jersey Institute of Technology, New Jersey 07102, USA
  • Received:2018-02-11 Online:2018-03-31 Published:2018-03-31

摘要:

为了提升可逆信息隐藏算法的信息嵌入容量,提出了一种目标像素自适应误差预测方法,基于自然图像局部区域内像素分布的一致性特征,自适应地学习目标像素周围像素点间的内在联系,并构建多元线性回归函数矩阵.该算法在实现目标像素点的准确预测时不是仅仅利用目标像素周围像素的简单算数组合预测目标像素的值,而是利用满足周围像素点一致性关系的线性关系函数.实验结果表明,相比其他先进的误差预测算法,基于多元线性回归的自适应图像可逆信息隐藏误差预测算法可以有效增强图像可逆信息嵌入能力.

关键词: 嵌入容量, 可逆信息隐藏, 多元线性回归, 自适应

Abstract:

An adaptive error prediction method based on multiple linear regression algorithm to improve the reversible information hiding capacity is proposed. The inner relationship among pixels around the object pixel is learned adaptively based on the consistency feature of pixels distributing in local area of natural images, and a multiple linear regression function matrix is built to express the relationship. The object pixel is predicted accurately with the linear function learned from its neighboring pixels, rather than simply with the arithmetic combination of surrounding pixels. Experimental results show that the multiple linear regression based adaptive image error prediction algorithm can effectively enhance the reversible data embedding capability compared to other advanced error prediction methods.

Key words: multiple linear regression, reversibility, reversible data hiding, embedding capacity

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