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Unified entropy-based sorting for reversible data hiding

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Abstract

Reversible data hiding schemes compete against each other for a sharply distributed prediction error histogram, usually realized by utilizing prediction strategies together with sorting technique that aims to estimate the local context complexity for each pixel to optimize the embedding order. Sorting techniques benefit prediction a lot by picking out pixels located in smooth areas. In this paper, a novel entropy-based sorting (EBS) scheme is proposed for reversible data hiding, which uses entropy measurement to characterize local context complexity for each image pixel. Futhermore, by extending the EBS technique to the two-dimensional case, it shows generalized abilities for multi-dimensinal RDH scenarios. Additionally, a new gradient-based tracking and weighting (GBTW) pixel prediction method is introduced to be combined with the EBS technique. Experimental results apparently indicate that our proposed method outperforms the previous state-of-arts counterparts significantly in terms of both the prediction accuracy and the overall embedding performance.

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Authors and Affiliations

  1. Nenghai Yu are with CAS Key Laboratory of Electro-magnetic Space Information, University of Science and Technology of China, Hefei, 230026, China

    Jiajia Xu, Weiming Zhang, Ruiqi Jiang & Nenghai Yu

Authors
  1. Jiajia Xu
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  2. Weiming Zhang
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  3. Ruiqi Jiang
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  4. Nenghai Yu
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Corresponding author

Correspondence to Weiming Zhang.

Additional information

This work was supported in part by the Natural Science Foundation of China under Grant 61170234 and Grant 61572452, in part by the Strategic Priority Research Program through the Chinese Academy of Sciences under Grant XDA06030601, and in part by the Science and Technology on Information Assurance Laboratory under Grant KJ-13-003.

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Xu, J., Zhang, W., Jiang, R. et al. Unified entropy-based sorting for reversible data hiding. Multimed Tools Appl 76, 3829–3850 (2017). https://doi.org/10.1007/s11042-016-3989-7

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  • DOI: https://doi.org/10.1007/s11042-016-3989-7

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