Hybrid local prediction error-based difference expansion reversible watermarking for medical images

Abstract

Embedding secret information into a cover media and extracting the information and the image without any distortion is known as reversible watermarking (RW). This paper analyzes the performance of hybrid local prediction error-based RW using difference expansion (DE). The cover medical image is split into non-overlapping blocks. The border pixels in each block are predicted using median edge detection (MED) prediction. The other pixels in the block are predicted using least square prediction, and the prediction error is expanded. The secret data are embedded into the cover medical image corresponding to the prediction error using the DE method. The predictor is also embedded into the cover medical image to recover the data at detection without any additional information. The simulation results show that, this method achieves better watermarked image quality and high embedding capacity when compared to other classical prediction methods: Median, MED, Rhombus and Gradient Adjusted Prediction.