Reversible Data Hiding With Pattern Adaptive Prediction

Abstract

Abstract In the area of reversible data hiding (RDH), one of the most popular techniques is prediction-error expansion (PEE), which hides data in the prediction errors with well-preserved image fidelity. The key to a successful PEE-based RDH implementation usually lies in prediction algorithms with high accuracy. Existing PEE-based RDH works often employ one single prediction algorithm, which is usually globally optimized, but with less consideration of the pixel distribution characteristics within local neighborhoods. In this manuscript, the technique of pattern adaptive prediction is proposed for pixel estimation according to the type of local binary pattern (LBP), which is obtained from the pixel’s eight neighborhood. Theoretically speaking, pattern-based predictors can be designed for each and every LBP patterns to create multiple prediction-error histograms (PEHs). However, the process of performance optimization with multiple PEHs requires extremely heavy computing power. To speed up the optimization process, LBP patterns are classified into various groups based on the degree of histogram concentration. Experiments demonstrate that the prediction accuracy is obviously improved and the image fidelity is well preserved.