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Sparse recovery based reversible data hiding method using the human visual system

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Abstract

In this paper, a novel sparse recovery based reversible data hiding (RDH) method using the human visual system (HVS) is presented. To improve the low accuracy of existing predictors, a sparse recovery based predictor is proposed. In the processes of sparse recovery, the most relevant neighbors can be adaptively chosen by using sparse representation to predict the current pixel accurately, and thus the concentrated prediction error histogram (PEH) is built to obtain good embedding performance. Moreover, to overcome the conflict between the embedding order of the traditional RDH method and the evaluation of HVS, a new embedding strategy based on just noticeable difference (JND) is designed. In this strategy, pixels are classified into sensitive and in-sensitive clusters according to JND values, and two corresponding PEHs are built. Accordingly, different inner regions of two PEHs are adjusted to meet the required embedding capacity, and the prediction error expansion (PEE) technique is utilized to embed data. Experimental results prove that the proposed method outperforms the state-of-the-art RDH methods, including JND related methods.

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Acknowledgements

This work was supported by Natural Science Foundation of China under Grant No. 61501270 and 61311140262, Natural Science Foundation of Ningbo under Grant No. 2017A610127 and 2016A610071. It was also sponsored by the K. C. Wong Magna Fund in Ningbo University.

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Authors and Affiliations

  1. College of Science and Technology, Ningbo University, Ningbo, 315212, China

    Ting Luo, Haiyong Xu & Wei Gao

  2. Faculty of Information Science and Engineering, Ningbo University, Ningbo, 315211, China

    Gangyi Jiang, Mei Yu, Haiyong Xu & Wei Gao

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  1. Ting Luo
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  2. Gangyi Jiang
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  3. Mei Yu
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  4. Haiyong Xu
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  5. Wei Gao
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Correspondence to Gangyi Jiang.

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Luo, T., Jiang, G., Yu, M. et al. Sparse recovery based reversible data hiding method using the human visual system. Multimed Tools Appl 77, 19027–19050 (2018). https://doi.org/10.1007/s11042-017-5356-8

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  • DOI: https://doi.org/10.1007/s11042-017-5356-8

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