  The conventional reversible data hiding (RDH) algorithms often consider the
host as a whole to embed a secret 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
optimization 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 efficient RDH in this paper, in which the raw host could be
partitioned into multiple subhosts such that each one can freely optimize and
use the data embedding parameters. Moreover, it enables us to apply different
RDH algorithms within different subhosts, 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 the
proposed 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 could outperform the original versions in most cases,
which has shown the superiority and applicability.
