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基于改进像素排序预测的大容量可逆数据隐藏

何文广 熊刚强 周珂 蔡洁

计算机应用研究2018,Vol.35Issue(1):266-271,6.
计算机应用研究2018,Vol.35Issue(1):266-271,6.DOI:10.3969/j.issn.1001-3695.2018.01.057

基于改进像素排序预测的大容量可逆数据隐藏

Larger capacity reversible data hiding using improved pixel-based pixel value ordering prediction

何文广 1熊刚强 1周珂 1蔡洁1

作者信息

  • 1. 广东医科大学信息工程学院,广东湛江524023
  • 折叠

摘要

Abstract

Pixel value ordering prediction based reversible data hiding can obtain high-fidelity marked image at the cost of limited embedding capacity.To achieve larger embedding capacity,Qu et al.proposed the idea of pixel-based pixel value ordering,where each pixel was predicted using its sorted context pixels.Although there is a great breakthrough in embedding capacity enlargement,Qu et al.'s single embedding strategy cannot fully exploit the redundancy of smooth region.In view of the fact that pixel-based pixel value ordering avoided locating the embedded pixel,this paper proposed to divide pixels in more detail and then presented a new exploiting modification direction based embedding strategy and the idea of two rounds data embedding correspondingly.For those target pixels from smooth region which guaranteed high prediction accuracy,the bidirectional modification based data embedding could increase the embedding capacity by 50%.This means that less target pixels from unsmooth region are needed for second round embedding and thus smaller region complexity threshold can be chosen to ensure higher prediction accuracy.Experimental results verify that this work can not only strengthen the advantage of Qu et al.'scheme in embedding capacity,but also help to improve the fidelity of marked image.What is more,the proposed scheme outperforms existing pixel value ordering prediction based schemes for higher fidelity in most cases.

关键词

可逆数据隐藏/像素排序/预测误差扩展/上下文/方向编码

Key words

reversible data hiding/pixel value ordering/prediction error expansion/context/exploiting modification direction

分类

信息技术与安全科学

引用本文复制引用

何文广,熊刚强,周珂,蔡洁..基于改进像素排序预测的大容量可逆数据隐藏[J].计算机应用研究,2018,35(1):266-271,6.

基金项目

国家自然科学基金资助项目(61170320,81201763) (61170320,81201763)

计算机应用研究

OA北大核心CSCDCSTPCD

1001-3695

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