A Novel Reversible Data Hiding Method Using Least-Square Based Predictor

As the performance of predictor is crucial to prediction-error based reversible data hiding, this study investigates a predictor using the least-square method to improve the prediction accuracy. The proposed predictor uses a checkerboard division to partition an image into two disjoint sets. One set is predicted by the other set using the prediction weights obtained by least-square method. The histogram-shifting method is then applied to the prediction errors to embed data. Because of better consideration for pixels´ correlation, the proposed method gains more prediction improvement, and thus raises embedding capacity with less distortion. Experimental results show that the proposed method outperforms the existing prediction-error based reversible data hiding methods in terms of hiding capacity and visual quality.