TY - JOUR
T1 - Rate and Distortion Optimization for Reversible Data Hiding Using Multiple Histogram Shifting
AU - Wang, Junxiang
AU - Ni, Jiangqun
AU - Zhang, Xing
AU - Shi, Yun Qing
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61379156 and Grant 61402209, in part by the National Research Foundation for the Doctoral Program of Higher Education of China under Grant 20120171110037, in part by the Key Program of Natural Science Foundation of Guangdong under Grant S2012020011114, and in part by Invention Patent and Industrialization Technology Demonstration Project of Jiangxi Province under Grant 20143BBM26113.
Publisher Copyright:
© 2016 IEEE.
PY - 2017/2
Y1 - 2017/2
N2 - Histogram shifting (HS) embedding as a typical reversible data hiding scheme is widely investigated due to its high quality of stego-image. For HS-based embedding, the selected side information, i.e., peak and zero bins, usually greatly affects the rate and distortion performance of the stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes utilize some empirical criterion to determine those side information, which generally could not lead to a globally optimal solution for reversible embedding. In this paper, based on the developed rate and distortion model, the problem of HS-based multiple embedding is formulated as the one of rate and distortion optimization. Two key propositions are then derived to facilitate the fast computation of distortion due to multiple shifting and narrow down the solution space, respectively. Finally, an evolutionary optimization algorithm, i.e., genetic algorithm is employed to search the nearly optimal zero and peak bins. For a given data payload, the proposed scheme could not only adaptively determine the proper number of peak and zero bin pairs but also their corresponding values for HS-based multiple reversible embedding. Compared with previous approaches, experimental results demonstrate the superiority of the proposed scheme in the terms of embedding capacity and stego-image quality.
AB - Histogram shifting (HS) embedding as a typical reversible data hiding scheme is widely investigated due to its high quality of stego-image. For HS-based embedding, the selected side information, i.e., peak and zero bins, usually greatly affects the rate and distortion performance of the stego-image. Due to the massive solution space and burden in distortion computation, conventional HS-based schemes utilize some empirical criterion to determine those side information, which generally could not lead to a globally optimal solution for reversible embedding. In this paper, based on the developed rate and distortion model, the problem of HS-based multiple embedding is formulated as the one of rate and distortion optimization. Two key propositions are then derived to facilitate the fast computation of distortion due to multiple shifting and narrow down the solution space, respectively. Finally, an evolutionary optimization algorithm, i.e., genetic algorithm is employed to search the nearly optimal zero and peak bins. For a given data payload, the proposed scheme could not only adaptively determine the proper number of peak and zero bin pairs but also their corresponding values for HS-based multiple reversible embedding. Compared with previous approaches, experimental results demonstrate the superiority of the proposed scheme in the terms of embedding capacity and stego-image quality.
KW - Genetic algorithm (GA)
KW - histogram shifting (HS)
KW - rate and distortion optimization
KW - reversible data hiding
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U2 - 10.1109/TCYB.2015.2514110
DO - 10.1109/TCYB.2015.2514110
M3 - Article
AN - SCOPUS:84961349893
SN - 2168-2267
VL - 47
SP - 315
EP - 326
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 2
M1 - 7393820
ER -