A Multiple Linear Regression Based High-Performance Error Prediction Method for Reversible Data Hiding

Abstract

In this paper, a high-performance error-prediction method based on Multiple Linear Regression (MLR) algorithm is first proposed to improve the performance of Reversible Data Hiding (RDH). The MLR matrix function that indicates the inner correlations between the pixels and its neighbors is established adaptively according to the consistency of pixels in local area of a natural image, and thus the object 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 object pixel with fixed parameters predictors through simple arithmetic combination of its surroundings pixel, experimental results show that the proposed method can provide a sparser prediction-error image for data embedding, and thus improves the performance of RDH more effectively than those state-of-the-art error prediction algorithms.