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High-fidelity reversible data hiding using novel comprehensive rhombus predictor

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Abstract

The rhombus mean predictor has been a popular and highly precise predictor commonly deployed for data hiding purposes. However, the rhombus predictor does not always produce the best prediction, for example, when any surrounding pixel is an outlier, because the predictor only calculates the mean of the surrounding pixels without considering their correlation. Therefore, this paper puts forward a comprehensive rhombus predictor (CRP) to take the correlation of the surrounding pixels into account when predicting the centre pixel. CRP adaptively selects the pixels based on their correlation and the characteristics of human visual system for a more precise prediction of the centre pixel. In addition, a highly efficient reversible data hiding (RDH) scheme is proposed using the CRP. The proposed RDH scheme first arranges the pixels in a sequence according to their predicted value by excluding high-complexity pixels. Subsequently, it partitions the sequence into multiple blocks so that the payload can be embedded according to their characteristics by adaptively selecting an embedding strategy. Experiment results demonstrate that the CRP provides higher performance than the existing non-causal related predictors in terms of prediction accuracy. In addition, our RDH based on CRP also outperforms the RDH methods built-upon non-causal related predictors in terms of embedding performance.

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Data availability

Data sharing does not apply to this article as used datasets are publicly available.

Notes

  1. http://sipi.usc.edu/database/database.php?volume=misc

  2. http://r0k.us/graphics/kodak/

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Acknowledgements

This research was partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A3049788), by Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2019H1D3A1A01101687, 2021H1D3A2A01099390) and by the project SERICS (PE00000014) under the NRRP MUR program funded by the EU—NGEU.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Delhi Technological University, Delhi, India

    Rajeev Kumar

  2. National Inter-University Consortium for Telecommunications (CNIT), Parma, Italy

    Roberto Caldelli

  3. Mercatorum University, Rome, Italy

    Roberto Caldelli

  4. School of Information Technology, Monash University Malaysia, Subang Jaya, Malaysia

    KokSheik Wong

  5. Department of CSE, National Institute of Technology Jalandhar, Punjab, India

    Aruna Malik

  6. Department of Software Convergence, Andong National University, Andong, Republic of Korea

    Rajeev Kumar & Ki-Hyun Jung

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  1. Rajeev Kumar
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  2. Roberto Caldelli
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  3. KokSheik Wong
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  4. Aruna Malik
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  5. Ki-Hyun Jung
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Correspondence to Ki-Hyun Jung.

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Kumar, R., Caldelli, R., Wong, K. et al. High-fidelity reversible data hiding using novel comprehensive rhombus predictor. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18797-6

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