A Novel Reversible Data Hiding Scheme Based on Pixel-Residual Histogram

Mengyao Xiao, Xiaolong Li, Yao Zhao, Bin Ma, Guodong Guo

Abstract: Prediction-error expansion (PEE) is the most popular reversible data hiding (RDH) technique due to its efficient capacity-distortion tradeoff. With the generated prediction-error histogram (PEH) and adaptively selected expansion bins, the image redundancy is well exploited by PEE. However, for the most widely used rhombus predictor, the rounding operation which groups different prediction-errors into one value is completely unnecessary. The embedding can be extended to a general case by removing the rounding operation, and more histogram bins can be derived for expansion with a new mapping mechanism. Therefore, in this article, instead of pixel prediction-error, we propose to compute the pixel residuals without the rounding operation, and a new embedding mechanism based on pixel-residual histogram (PRH) modification is devised. In PRH, four bins correspond to one bin in PEH. Then, different from the one-to-one mapping between the prediction-error and pixel modification, a four-to-one mapping between the pixel-residual and pixel modification is established, and the performance is optimized by adaptively selecting four expansion bin pairs for embedding. Since more modification selections are considered, better performance can be obtained. Moreover, the proposed scheme is extended to the two-dimensional (2D) histogram and multiple histograms based embedding, and the performance is further enhanced. The superiority of the proposed method is experimentally verified by comparing it with some state-of-the-art works.