Improved reversible information hiding with adaptive prediction

With the advance of time and scholars pay close attention to prediction-error expansion in reversible data hiding, a large number of adaptive prediction-error expansion algorithms are emerging. Previous methods often use closed pixel correlation to predict pixels, but the prediction accuracy is low in the image texture region. In this paper, we sum a reversible data hiding framework based on prediction-error expansion at first. Depending on this framework, we proposed an iterative regularization method to predict pixels by applying a first order difference edge preserving operator predictor. The continuous iterative algorithm is used to modify the prediction results to obtain the optimal and stable prediction results. In this way, the overall prediction effect of the image is improved, especially in the texture region of the image. Moreover, the first order difference sum is used to sort the order of the embedded information, so as to improve the quality of the stego image. The experimental results show the mathod proposed is better than some state-of-the-art methods.