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An Improved Reversible Data Hiding Scheme with Large Payload Based on Image Local-Complexity

  • Fang Cao
  • Yalei Zhang
  • Bowen An
  • Heng Yao
  • Zhenjun Tang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11066)

Abstract

In this paper, a reversible data hiding scheme for digital images with high hiding capacity is proposed. Original image is segmented into smooth and rough regions based on local complexity. In order to achieve higher hiding capacity, we embed three bits into each pixel belonging to smooth region with lower local complexity and one bit is embedded into each pixel of rough region, which can effectively exploit more redundancy during data embedding compared with conventional methods of prediction error expansion (PEE). Additionally, the pixel selection mechanism is applied to reduce the number of shifted pixels, which leads to high visual quality of stego image. Experimental results show that, our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.

Keywords

Reversible data hiding Prediction error expansion Hiding capacity Image quality 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61171126, 61272452, 61702332, U1636101, 61562007), Ministry of Transport and Applied Basic Research Projects (2014329810060), and Science & Technology Program of Shanghai Maritime University (20130479), Natural Science Foundation of Guangxi (2017GXNSFAA198222), and Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS15-03).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Fang Cao
    • 1
  • Yalei Zhang
    • 2
  • Bowen An
    • 1
  • Heng Yao
    • 2
  • Zhenjun Tang
    • 3
  1. 1.College of Information EngineeringShanghai Maritime UniversityShanghaiChina
  2. 2.School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
  3. 3.Guangxi Key Lab of Multi-Source Information Mining & SecurityGuangxi Normal UniversityGuilinChina

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