Reversible data hiding based on PDE predictor

• Propose a new predictor based on PDE (partial difference expanding) which is image-content dependent.
• More accurate than the conventional predictors and hence result in better performance than the state-of-the-art reversible data hiding methods.
• High applicability since PDE predictor can be well incorporated in various PEE schemes.

In this paper, we propose a prediction-error expansion based reversible data hiding by using a new predictor based on partial differential equation (PDE). For a given pixel, PDE predictor uses the mean of its four nearest neighboring pixels as initial prediction, and then iteratively updates the prediction until the value goes stable. Specifically, for each pixel, by calculating the gradients of four directions, the direction with small magnitude of gradient will be weighted larger in the iteration process, and finally a more accurate prediction can be obtained. Since PDE predictor can better exploit image redundancy, the proposed method introduces less distortion for embedding the same payload. Experimental results show that our method outperforms some state-of-the-art methods.