Reversible Data Hiding Based on Adaptive Embedding with Local Complexity

In recent years, most reversible data hiding (RDH) algorithms have considered the impact of texture information on embedding performance. The distortion caused by embedding secret data in the image’s smooth region is much less than in the non-smooth region. It is because embedding secret data in the smooth region corresponds to fewer invalid shifting pixels (ISPs) in histogram shifting. However, though effective, the local complexity is not calculated precisely enough, which results in inaccurate texture division and does not considerably reduce distortion. Therefore, a new RDH scheme based on adaptive embedding with local complexity (AELC) is proposed to improve the embedding performance effectively. Specifically, the cover image is divided into two subsets by the checkerboard pattern. Then the local complexity of each pixel is computed by the correlation between adjacent pixels (CBAP). Finally, secret data are adaptive and preferentially embedded into the regions with lower local complexity in each subset. Experimental results show that the proposed algorithm performs best regarding invalid shifted pixels, maximum embedding capacity (EC), and peak signal-to-noise ratio (PSNR) compared to some state-of-the-art RDH methods.