Adaptive Predictor Structure Based Interpolation For Reversible Data Hiding

Abstract. In this paper, we present an additive prediction error expan-sion (PEE) based reversible data hiding scheme that gives overall low distortion and relatively high embedding capacity. Recently reported in-terpolation based PEE method uses fixed order predictor that fails to ex-ploit the correlation between the neighborhood pixels and the unknown pixel (to be interpolated). We observed that embedding capacity and distortion of PEE based algorithm depends on the prediction accuracy of the predictor. In view of this observation, we propose an interpolation based method that predicts pixels using predictors of different structure and order. Moreover, we use only original pixels for interpolation. Ex-perimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms both in terms of embedding capacity and Peak Signal to Noise Ratio