An Efficient CNN-based Prediction for Reversible Data Hiding
Mike Zhiren Wu, Shijun Xiang · 2023
In the field of reversible data hiding (RDH), how to design an efficient image prediction method is an enduring research topic. In this paper, we propose a new CNN-based predictor consisting of an efficient image division strategy and a well-designed prediction network. The image division strategy optimizes the distribution of pixels belonging to different sets, which increases the amount of available adjacent pixels in the image prediction. In addition, with the utilization of the well-designed compensation module, the prediction network performs better and consumes less memory. The experiment results demonstrate that our approach achieves better prediction performance compared with the existing predictors. Furthermore, we have developed an RDH algorithm by combining the proposed CNN-based predictor with the location-based pixel value ordering (LPVO) embedding strategy. This RDH algorithm outperforms the state-of-the-art predictor-based RDH algorithm in embedding performance.