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Improved reversible data hiding based on PVO and adaptive pairwise embedding

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Abstract

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the cover image is first divided into non-overlapping blocks with equal size. Then, the pixel values in each block are sorted in ascending order. Next, take the second largest/samllest pixel value as a prediction of the largest/samllest pixel value to derive two prediction errors. Finally, the data embedding is constructed by modifying the generated prediction errors of each block. After data embedding, the PVO of each block is unchanged, which guarantees the reversibility. Our key observation is that, in each block, the modification for the two prediction errors is independent without exploiting the correlation between them, although they are closely correlated with each other. In light of this, an improved PVO-based RDH method is proposed in this work. The two prediction errors of each block are considered as a pair, and the pairs are modified for data embedding based on adaptive two-dimensional histogram modification. The proposed method is experimentally verified better than the original PVO-based method and some of its improvements.

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Acknowledgements

This work was supported by the National Key Research and Development of China (No. 2016YFB0800404), the National Science Foundation of China (Nos. 61572052, U1736213 and 61532005), and the Fundamental Research Funds for the Central Universities (No. 2018JBZ001).

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Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China

    Haorui Wu, Xiaolong Li, Yao Zhao & Rongrong Ni

  2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing, 100044, China

    Haorui Wu, Xiaolong Li, Yao Zhao & Rongrong Ni

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  1. Haorui Wu
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  2. Xiaolong Li
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  3. Yao Zhao
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  4. Rongrong Ni
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Correspondence to Yao Zhao.

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Wu, H., Li, X., Zhao, Y. et al. Improved reversible data hiding based on PVO and adaptive pairwise embedding. J Real-Time Image Proc 16, 685–695 (2019). https://doi.org/10.1007/s11554-019-00867-w

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