Generalized Skewed Histogram Shifting Based Reversible Data Hiding by Differential Evolution

Skewed histogram shifting (SHS) is an efficient scheme in reversible data hiding (RDH) research. By employing a pair of symmetric predictors which averages part of sorted pixels around the to-be-predicted pixel, two skewed histograms are generated. With the embedding and shifting directions toward the short tail of the two histograms, SHS reduces many invalid modifications. However, the design of the symmetric predictors pair is strictly constrained, which seriously degrades the performance on both embedding capacity and distortion of this SHS scheme. In this work, we propose a generalized SHS model to remove the weight and symmetry constraints. With the help of differential evolution algorithm, the optimized parameters are obtained in a short period of time, avoiding wasting time using exhaustive search. What is more, adaptive pairwise mapping and embedding bin selection are also realized by adding parameters into the evolutionary process, which greatly improve the embedding performance without increasing too much computational complexity. Experiments demonstrate the superiority of our method by comparing it with state-of-the-art RDH schemes.