Improving pairwise PEE via hybrid-dimensional histogram generation and adaptive mapping selection

Pairwise prediction-error expansion (pairwise PEE) is a recent technique for the high-dimensional reversible data hiding. However, in the absence of adaptive embedding, its potential has not been fully exploited. In this paper, we propose the adaptive pixel pairing (APP) and the adaptive mapping selection for the enhancement of pairwise PEE. Our motivation is twofold: building a sharper 2D histogram and designing the effective 2D mapping for it. In APP, we consider to increase the similarity between pixels in a pair, by excluding the rough pixels from pairing and only putting the smooth pixels into pairs. In this way, the pixels in a pair have a larger possibility of being equal, and thus the resulted 2D prediction-error histogram (PEH) has lower entropy. Next, the adaptive mapping selection mechanism is introduced to properly determine the optimal modification, based on “whether it fits for the resulted PEH” rather than heuristic experience. The experimental results show that the proposed method has a significant improvement over the pairwise PEE.