Maximally Separated Averages Prediction for High Fidelity Reversible Data Hiding

Abstract

Recently pixel pairing and pixel sorting/selection have been used in prediction-error expansion based reversible data hiding schemes to generate low entropy prediction-error histograms (PEH) necessary for achieving high fidelity. Such schemes generally use the four-neighbor average rhombus predictor as it allows pixel sorting and flexible pixel pairing. In this paper, we propose the maximally separated averages (MSA) predictor that uses the four-neighborhood context. It can replace the rhombus predictor in pixel pairing and sorting based schemes for lowering PEH entropy further to achieve higher performance. At each pixel location, we choose the two maximally separated average values and decide either on using one of them as the predicted value or on avoiding prediction at the pixel location. This is based on the observation that the prediction-error sequence entropy decreases with the increase in the separation between the two average values. Experimental results demonstrate that the state-of-the-art schemes achieve considerable performance improvement by using the proposed MSA predictor.