DocumentCode
3377659
Title
Double-threshold reversible data hiding
Author
Xuan, Guorong ; Shi, Yun Q. ; Teng, Jianzhong ; Tong, Xuefeng ; Chai, Peiqi
Author_Institution
Dept. of Comput. Sci., Tongji Univ., Shanghai, China
fYear
2010
fDate
May 30 2010-June 2 2010
Firstpage
1129
Lastpage
1132
Abstract
This proposed scheme reversibly embeds data into image prediction-errors by using histogram-pair method with double thresholds (embedding threshold and fluctuation threshold). The embedding threshold is used to select only those prediction-errors, whose magnitude does not exceed this threshold, for possible reversible data hiding. The fluctuation threshold is used to select only those prediction-errors, whose associated neighbor fluctuation does not exceed this threshold, for possible reversible data hiding. Only when both thresholds are satisfied the reversible data hiding is carried out. Image gray level histogram modification is conducted to shrink the image histogram towards the center to avoid underflow and/or overflow only when this is necessary. The required bookkeeping data are embedded together with pure payload for original image recovery late. The experimental results have demonstrated that the proposed scheme outperforms recently published reversible image data hiding schemes in terms of the highest PSNR of marked image vs. original image at given pure payloads.
Keywords
data encapsulation; image processing; double-threshold reversible data hiding; histogram-pair method; image gray level histogram modification; image prediction-errors; image recovery; Authentication; Computer science; Data encapsulation; Data mining; Discrete wavelet transforms; Fluctuations; Histograms; PSNR; Payloads; Pixel; gray level histogram modification; histogram pair scheme; neighborhood fluctuation; prediction error; reversible image data hiding;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on
Conference_Location
Paris
Print_ISBN
978-1-4244-5308-5
Electronic_ISBN
978-1-4244-5309-2
Type
conf
DOI
10.1109/ISCAS.2010.5537323
Filename
5537323
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