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A generalized pixel value ordering data hiding with adaptive embedding capability

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Abstract

In this paper, we proposed an improvisation to Li et al.’s pixel value ordering (PVO) method and called it as generalized PVO (GePVO), which provides a suitable expansion strategy using the image redundancy more abundantly. GePVO makes use of a reference pixel and calculates prediction errors using both sides of the block in different modes (one-way GePVO, bidirectional GePVO, round-trip GePVO). The experimental results demonstrate that GePVO had better performance in terms of an increased embedding capacity compared to the other data hiding methods. When compared to Li et al.’s method, the bidirectional GePVO led to an increase in the embedding capacity by 1.8 times and the round-trip GePVO by three times. We contend that the GePVO method made the PVO method more flexible and unbound in terms of embedding capacity while keeping the image quality intact. Performance comparison with other reversible data hiding schemes is presented to demonstrate the validity of the proposed method.

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Acknowledgments

We are grateful to all students and teachers who participated in this study and all the colleagues working to realize this project.

Funding

This research was partially supported by the (Ministry of Science and Technology, Taiwan, Republic of China) under the Grant (MOST 106-2221-E-324-006-MY2).

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Authors and Affiliations

  1. Department of Information Management, Chaoyang University of Technology, No. 168, Jifeng E. Rd., Wufeng District, Taichung, 41349, Taiwan

    Chin-Feng Lee, Somya Agrawal & Yu-Ju Tseng

  2. Department of Management Information Systems, National Chung Hsing University, No. 145, Xingda Road, South District, Taichung, 40227, Taiwan

    Jau-Ji Shen & Yu-Chi Kao

Authors
  1. Chin-Feng Lee
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  2. Jau-Ji Shen
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  3. Somya Agrawal
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  4. Yu-Ju Tseng
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  5. Yu-Chi Kao
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Correspondence to Somya Agrawal.

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Lee, CF., Shen, JJ., Agrawal, S. et al. A generalized pixel value ordering data hiding with adaptive embedding capability. J Supercomput 76, 2683–2714 (2020). https://doi.org/10.1007/s11227-019-03052-9

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  • DOI: https://doi.org/10.1007/s11227-019-03052-9

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