A reversible data hiding based on adaptive prediction technique and histogram shifting

Abstract

Reversible data hiding recovers the original image from the stego-image without distortion after data extraction. In this paper, we propose a novel reversible data hiding method based on adaptive prediction techniques and histogram shifting. Because most natural images always contain edges, it is not suitable to predict these pixels using existing prediction methods. For more precise prediction, two prediction methods are adaptively used to calculate prediction error according to the characteristic of a pixel. As a result, two prediction error histograms are built. One is for pixels located at edges, and the other is for the rest pixels. Data are embedded in the image by using histogram shifting method. In addition, a new sorting method is applied to histogram shifting, which considers the differences of all pixel pairs in the neighborhood and better reflects the correlation among pixels. Through the sorting method, the prediction errors with small absolute values are arranged in the front and more embeddable pixels are preferentially processed. Therefore, the number of shifting pixels is decreased if the peaks in the histograms are all dealt with or the capacity is satisfied, which is beneficial to distortion reduction. Experimental results demonstrate that the proposed method acquires greater capacity and higher quality compared with other state-of-the-art schemes.