Multimedia Tools and Applications

, Volume 76, Issue 6, pp 8781–8801 | Cite as

A novel blind color image watermarking based on Contourlet transform and Hessenberg decomposition

  • Qingtang SuEmail author
  • Gang Wang
  • Gaohuan Lv
  • Xiaofeng Zhang
  • Guanlong Deng
  • Beijing Chen


In this paper, a novel blind color image watermarking based on Contourlet transform and Hessenberg decomposition is proposed to protect digital copyright of color image. Firstly, each color channel of the host image is transformed by Contourlet transform and its low frequency sub-band is divided into 4 × 4 non-overlap coefficient block. Secondly, the coefficient block selected by MD5-based Hash pseudo-random algorithm is decomposed by Hessenberg decomposition. Thirdly, the watermark information permuted by Arnold transform is embedded into the biggest energy element of the upper Hessenberg matrix by quantization technique. In extraction process, the quantization strength is used for blindly extracting watermark information from the attacked host image without the help of any original image. The results show that the proposed scheme has higher imperceptibility and robustness against most common image attacks in comparison with other related methods.


Contourlet transform Hessenberg matrix Color image watermark Blind watermarking 



The research was partially supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), NNSF from China (61202111, 61471185, 61403180, 61472172, 61502218, 61572258), Natural Science Foundation of Shandong Province (ZR2014FM005), Shandong Province Higher Educational Science and Technology Program (J14LN20, J12LN05), Doctoral Foundation of Ludong University (LY2014034), Shandong Province Science and Technology Plan Projects (2015GSF116001). The authors would like to thank anonymous referees for their valuable comments and suggestions which lead to substantial improvements of this paper.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Qingtang Su
    • 1
    Email author
  • Gang Wang
    • 1
  • Gaohuan Lv
    • 1
  • Xiaofeng Zhang
    • 1
  • Guanlong Deng
    • 1
  • Beijing Chen
    • 2
  1. 1.School of Information Science and EngineeringLudong UniversityShandongChina
  2. 2.School of Computer & SoftwareNanjing University of Information Science & TechnologyNanjingChina

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