Multiple histogram based adaptive pairwise prediction-error modification for efficient reversible image watermarking

Abstract

The prediction-error expansion (PEE) based reversible data hiding (RDH) methods mainly contain two steps: prediction-error histogram (PEH) generation and PEH modification. Multiple histograms modification (MHM) and pairwise PEE are two efficient methods used in PEH modification, which can greatly reduce the distortion to the cover image under given payloads. However, these two effective methods are not used together for their conflict on causality and large computational complexity. To address the above two problems, in this paper, a group of non-causal predictors with a specially designed complexity metric are proposed so that the conflict on causality is resolved. Then, the original optimization problem to determine the parameters of MHM and pairwise PEE is decomposed into two easier ones, which are addressed by a step-by-step optimization. Several efficient constraints are used to narrow the search range for the optimal parameters while minimizing the performance degradation. As a result, the proposed method can obtain high quality on the embedded images, that is, the average PSNR of the 8 standard 512 × 512 sized grayscale images from the USC-SIPI image database can reach 60.43 dB for the payload of 10,000 bits, while the running time for the proposed algorithm is acceptable. Experimental results show that the proposed method outperforms many kinds of RDH schemes, and achieves an obvious increment in fidelity over a series of state-of-the-art RDH schemes.