Optimal structural similarity constraint for reversible data hiding

• DOI: 10.1007/s11042-016-3850-z
• Publication Date: 2016-09-10T02:43:47Z
• Authors: Jiajia Xu; Weiming Zhang; Ruiqi Jiang; Xiaocheng Hu; Nenghai Yu
• Keywords: Peak signal-to-noise ratio; Similarity (geometry); Distortion (music)

Until now, most reversible data hiding techniques have been evaluated by peak signal-to-noise ratio(PSNR), which based on mean squared error(MSE). Unfortunately, MSE turns out to be an extremely poor measure when the purpose is to predict perceived signal fidelity or quality. The structural similarity (SSIM) index has gained widespread popularity as an alternative motivating principle for the design of image quality measures. How to utilize the characterize of SSIM to design RDH algorithm is very critical. In this paper, we propose an optimal RDH algorithm under structural similarity constraint. Firstly, we deduce the metric of the structural similarity constraint, and further we prove it does't hold non-crossing-edges property. Secondly, we construct the rate-distortion function of optimal structural similarity constraint, which is equivalent to minimize the average distortion for a given embedding rate, and then we can obtain the optimal transition probability matrix under the structural similarity constraint. Comparing with previous RDH, our method have gained the improvement of SSIM about 1.89 % on average. Experiments show that our proposed method outperforms the state-of-arts performance in SSIM.