High Precision Error Prediction Algorithm Based on Ridge Regression Predictor for Reversible Data Hiding

Abstract

An efficient predictor is crucial for high embedding capacity and low image distortion. In this letter, a ridge regression-based high precision error prediction algorithm for reversible data hiding is proposed. The ridge regression is a penalized least-square algorithm, which solves the overfitting problem of the least-square method. Reversible data hiding based on ridge regression predictor minimizes the residual sum of squares between predicted and target pixels subject to the constraint expressed in terms of the L2-norm. Compared to a least-square-based predictor, the ridge regression-based predictor can obtain more small prediction errors, proving that the proposed method has a higher accuracy. In addition, the eight neighbor pixels of the target pixels and their two different combinations are selected as training and support sets, respectively. This selection scheme further improves the prediction accuracy. Experimental results show that the proposed method outperforms state-of-the-art adaptive reversible data hiding in terms of prediction accuracy and embedding performance.