Efficient reversible data hiding scheme based on prediction-error expansion and optimal parameters dynamic selection

Abstract

As a new research hotspot in the realm of information security, reversible data hiding (RDH) technology has garnered a lot of attention and has been widely used in recent years. An RDH scheme with a high embedding capacity (EC) based on prediction-error expansion is proposed. Specifically, we first divide the host image into two regions of the same size. Then the local complexity value of each pixel is calculated. Furthermore, we employ five parameters, i.e., the local complexity threshold, the embedding bins on the left and right of the prediction error histogram (PEH) and their number to calculate the distortion of the image. The parameters corresponding to the minimal distortion are the optimal parameters. Finally, we embed secret data into the optimal embedding bins on PEH and obtain the best embedding performance. The experimental results reveal that our scheme has a higher EC and better performance than some other state-of-the-art methods.