Abstract
In this paper, we propose a prediction-error expansion based reversible data hiding scheme by incorporating non-local means (NLM) prediction. The traditional local predictors reported in literatures rely on the local correlation and behave badly in predicting textural pixels. To remedy this, we propose to use NLM to achieve better prediction in texture regions and globally utilize the potential self-similarity contained in the image itself. More specifically, the textural pixels distinguished by its local complexity are predicted by NLM while the smooth pixels having high local correlation are predicted by a local predictor. The incorporation of NLM makes the proposed method possible to achieve accurate predictions in both smooth and texture regions. Optimal parameters in the method are obtained by minimizing the prediction-error entropy. Experimental results show that the proposed method can yield an improvement compared with some state-of-the-art methods.
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Acknowledgement
This work is supported by the National Science Foundation of China (Nos. 61502160, 61472131, 61272546), Science and Technology Key Projects of Hunan Province (2015TP1004).
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Fang, Y., Ou, B. (2016). Reversible Data Hiding Using Non-local Means Prediction. In: Carretero, J., et al. Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10049. Springer, Cham. https://doi.org/10.1007/978-3-319-49956-7_10
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DOI: https://doi.org/10.1007/978-3-319-49956-7_10
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