Reversible data hiding based on multi‐predictor and adaptive expansion

Adaptive embedding plays an important role in improving the embedding performance of reversible data hiding and it is usually realized by modifying the prediction‐errors discriminately. By extending the skewed histogram shifting technique which uses a pair of extreme predictions to determine whether the target pixel should be predicted or not, this paper realizes another form of adaptive embedding, that is, adaptive prediction‐error generation. Specifically, it is proposed to adaptively determine the pair of extreme predictions according to image content. Each pair of extreme predictions is first evaluated by the introduced distortion per embedding one bit. Then, an efficient mechanism to determine the best pair of extreme predictions for pixels with a given local complexity is designed. With such a mechanism solving the computational problem, it is also proposed to extend the context to obtain more pairs of extreme predictions such that more precise multi‐predictor can be realized. With obtained errors, the best expansion bins are determined to achieve a more comprehensive self‐adaption. Experimental results demonstrate that the proposed scheme achieves better capacity‐distortion performance and outperforms a series of state‐of‐the‐art schemes.