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Adaptive error prediction method based on multiple linear regression for reversible data hiding

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Abstract

To improve the prediction accuracy, this paper proposes an adaptive error prediction method based on multiple linear regression (MLR) algorithm. The MLR matrix function that indicates the inner correlations between the pixels and their neighbors is established adaptively according to the consistency of pixels in local area of a natural image, and thus the objected pixel is predicted accurately with the achieved MLR function that denotes the consistency of the neighboring pixels. Compared with the conventional methods that predict the objected pixel with fixed predictors through simple arithmetic combination of its surroundings pixel, the proposed method can provide a comparatively spare prediction-error image for data embedding, and thus can improve the performance of reversible data hiding. Experimental results show that the proposed method outperforms most state-of-the-art error prediction algorithms.

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Authors and Affiliations

  1. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250300, China

    Bin Ma, Xiaoyu Wang, Qi Li, Bing Li, Jian Li & Chunpeng Wang

  2. New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Yunqing Shi

Authors
  1. Bin Ma
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  2. Xiaoyu Wang
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  3. Qi Li
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  4. Bing Li
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  5. Jian Li
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  6. Chunpeng Wang
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  7. Yunqing Shi
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Correspondence to Xiaoyu Wang.

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Ma, B., Wang, X., Li, Q. et al. Adaptive error prediction method based on multiple linear regression for reversible data hiding. J Real-Time Image Proc 16, 821–834 (2019). https://doi.org/10.1007/s11554-019-00891-w

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  • DOI: https://doi.org/10.1007/s11554-019-00891-w

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