Efficient HS based Reversible Data Hiding Using Multi-feature Complexity Measure and Optimized Histogram

Junxiang Wang, Jiangqun Ni, Xing Zhang

Abstract: Histogram-shifting (HS) embedding as a most successful reversible data hiding (RDH) scheme is widely investigated. How to take advantage of covers' redundancyis one of the key issues for performance improvement of RDH. Among them, sorting technique isof practically importance, which uses in prioritysmooth areas with high correlation to hide message. For conventional schemes, usually a single empirical feature in the context of local cover object, such as local variance, is utilized as complexity measure for sorting, which may not effectively exploit the covers' correlations and thus lead to limited performance improvement. In this paper, a general framework for optimal construction of complexity measure with multi-feature is developed, which includes optimal feature selection and weight parameters determination based on an optimization model. In addition, the optimal truncation point for the top of the sorted cover is determined based on the payload constraint to construct the optimal histogram for further performance improvement of HS embedding. Compared with previous approaches, experimental results demonstrate the superiority of the proposed scheme.