A high capacity reversible data hiding through multi-directional gradient prediction, non-linear regression analysis and embedding selection

Abstract

Abstract The technique of reversible data hiding enables an original image to be restored from a stego-image with no loss of host information, and it is known as a reversible data hiding algorithm (RDH). Our goal is to design a method to predict pixels effectively, because the more accurate the prediction is, the more concentrated the histogram is, and it minimizes shifting to avoid distortion. In this paper, we propose a new multi-directional gradient prediction method to generate more accurate prediction results. In embedding stage, according to the embedding capacity of information, we generate the best decision based on non-linear regression analysis, which can differentiate between embedding region and non-embedding region to reduce needless shifting. Finally, we utilize the automatic embedding range decision. With sorting by the amount of regional variance, the easier predicted region is prior for embedding, and the quality of the image is improved after embedding. To evaluate the proposed reversible hiding scheme, we compared other methods on different pictures. Results show that the proposed scheme can embed much more data with less distortion.

Keywords

• Reversible data hiding (RDH)
• Non-linear regression analysis
• Multi-directional variation prediction