Adaptive Pairwise Prediction-Error Expansion and Multiple Histograms Modification for Reversible Data Hiding

In recent years, high-dimensional histogram modification based reversible data hiding for images has drawn much attention among researchers. Pairwise prediction-error expansion (pairwise PEE) achieves great performance by exploiting the correlations among prediction-errors, in which a two-dimensional (2D) prediction-error histogram (PEH) is generated, and then modified based on a specific 2D modification mapping for reversible embedding. However, the used 2D mapping is fixed and heuristically designed without considering the image content, so the embedding performance of pairwise PEE can be further improved. To this end, the adaptive pairwise PEE (APPEE) is proposed in this paper to adaptively design the 2D mapping according to the distribution of 2D PEH, such that a better embedding performance is derived. To further improve the embedding performance, the proposed APPEE is extended from one single 2D PEH to multiple 2D PEHs generated based on different local complexities, in which multiple content-based 2D mappings are designed adaptively and individually for each 2D PEH. Experimental results show that the APPEE for either one single 2D PEH or multiple 2D PEHs significantly outperforms the conventional pairwise PEE. Simultaneously, the superiority of the proposed method is also experimentally verified compared with some other state-of-the-art works.