DocumentCode :
2373535
Title :
Adaptive reversible data hiding through autoregression
Author :
Jingyang Wen ; Jinli Lei ; Yi Wan
Author_Institution :
Inst. for Signals & Inf. Process., Lanzhou Univ., Lanzhou, China
fYear :
2012
fDate :
23-25 March 2012
Firstpage :
831
Lastpage :
838
Abstract :
An adaptive reversible data hiding method through autoregression is presented in this paper. In the proposed algorithm, we focus on the image pixel value prediction, which plays a key role in the data embedding process. Unlike conventional data hiding techniques, a threshold is adjusted for each image to divide all pixels into two regions: the smooth region and the texture region. Then the proposed algorithm optimally estimates the coefficients of autoregression model for pixel value prediction through least-squares minimization. The prediction error is adaptively minimized to achieve high prediction accuracy so that more redundancy in the image is exploited to achieve very high data embedding capacity while keeping the distortion low. Experimental results show that the proposed algorithm outperforms typical state-of-the-art methods in general.
Keywords :
autoregressive processes; data encapsulation; image coding; image segmentation; image texture; least squares approximations; minimisation; adaptive reversible data hiding; autoregression model coefficients; data embedding process; image pixel value prediction; image redundancy; image thresholds; least-squares minimization; optimal estimation; prediction accuracy; prediction error adaptive minimization; smooth region; texture region; Biomedical imaging; Histograms; Image coding; PSNR; Payloads; Prediction algorithms; Watermarking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Technology (ICIST), 2012 International Conference on
Conference_Location :
Hubei
Print_ISBN :
978-1-4577-0343-0
Type :
conf
DOI :
10.1109/ICIST.2012.6221765
Filename :
6221765
Link To Document :
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