Ensemble Reversible Data Hiding

The conventional reversible data hiding (RDH) algorithms often consider the host as a whole to embed a payload. In order to achieve satisfactory rate-distortion performance, the secret bits are embedded into the noise-like component of the host such as prediction errors. From the rate-distortion view, it may be not optimal since the data embedding units use the identical parameters. This motivates us to present a segmented data embedding strategy for RDH in this paper, in which the raw host could be partitioned into multiple sub-hosts such that each one can freely optimize and use the embedding parameters. Moreover, it enables us to apply different RDH algorithms within different sub-hosts, which is defined as ensemble. Notice that, the ensemble defined here is different from that in machine learning. Accordingly, the conventional operation corresponds to a special case of our work. Since it is a general strategy, we combine some state-of-the-art algorithms to construct a new system using the proposed embedding strategy to evaluate the rate-distortion performance. Experimental results have shown that, the ensemble RDH system outperforms the original versions, which has shown the superiority and applicability.