A Multiple Linear Regression Based High-Accuracy Error Prediction Algorithm for Reversible Data Hiding

In reversible data hiding, the higher embedding capacity and lower distortion are simultaneously expected. Hence, the precise and efficient error-prediction algorithm is essential and crucial. In this paper, a high-performance error-prediction method based on Multiple Linear Regression (MLR) algorithm is proposed to improve the performance of Reversible Data Hiding (RDH). The MLR matrix function that indicates the inner correlations between the pixels and their neighbors is established adaptively according to the consistency of pixels in local area of a natural image, and thus the targeted pixel is predicted accurately with the achieved MLR function that satisfies the consistency of the neighboring pixels. Compared with conventional methods that only predict the targeted pixel with fixed predictors through simple arithmetic combination of its surroundings pixel, the proposed method can provide a sparser prediction-error image for data embedding, and thus improves the performance of RDH. Experimental results have shown that the proposed method outperform the state-of-the-art error prediction algorithms.