XU Jiajia, ZHANG Weiming, JIANG Ruiqi, YU Nenghai, HU Xiaocheng. Minimum Structural Similarity Distortion for Reversible Data Hiding[J]. Chinese Journal of Electronics, 2018, 27(3): 582-587. doi: 10.1049/cje.2018.03.012
Citation: XU Jiajia, ZHANG Weiming, JIANG Ruiqi, YU Nenghai, HU Xiaocheng. Minimum Structural Similarity Distortion for Reversible Data Hiding[J]. Chinese Journal of Electronics, 2018, 27(3): 582-587. doi: 10.1049/cje.2018.03.012

Minimum Structural Similarity Distortion for Reversible Data Hiding

doi: 10.1049/cje.2018.03.012
Funds:  This work is supported by the Natural Science Foundation of China (No.61170234, No.61572452) and the Strategic Priority Research Program through the Chinese Academy of Sciences (No.XDA06030601).
  • Received Date: 2015-12-28
  • Rev Recd Date: 2016-04-19
  • Publish Date: 2018-05-10
  • Until now, most Reversible data hiding (RDH) techniques have been evaluated by Peak signal-tonoise 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. We propose an optimal RDH algorithm under structural similarity constraint. We deduce the metric of the structural similarity constraint, and further we prove it does not hold Non-crossing-edges (NCE) property. 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. Experiments show that our proposed method can be used to improve the performance of previous RDH schemes evaluated by SSIM.
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