TY - GEN
T1 - Optimal histogram-pair and prediction-error based image reversible data hiding
AU - Xuan, Guorong
AU - Tong, Xuefeng
AU - Teng, Jianzhong
AU - Zhang, Xiaojie
AU - Shi, Yun Qing
N1 - Funding Information:
This research is largely supported by Shanghai City Board of education scientific research innovation projects (12ZZ033) and National Natural Science Foundation of China (NSFC) on project (90304017).
PY - 2013
Y1 - 2013
N2 - This proposed scheme reversibly embeds data into image prediction-errors by using histogram-pair method with the following four thresholds for optimal performance: embedding threshold, fluctuation threshold, left- and right-histogram shrinking thresholds. 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. The left- and right-histogram shrinking thresholds are used to possibly shrink histogram from the left and right, respectively, by a certain amount for reversible data hiding. Only when all of four thresholds are satisfied the reversible data hiding is carried out. Different from our previous work, the image gray level histogram shrinking towards the center is not only for avoiding underflow and/or overflow but also for optimum performance. The required bookkeeping data are embedded together with pure payload for original image recovery. The experimental results on four popularly utilized test images (Lena, Barbara, Baboon, Airplane) and one of the JPEG2000 test image (Woman, whose histogram does not have zero points in the whole range of gray levels, and has peaks at its both ends) have demonstrated that the proposed scheme outperforms recently published reversible image data hiding schemes in terms of the highest PSNR of marked image verses original image at given pure payloads.
AB - This proposed scheme reversibly embeds data into image prediction-errors by using histogram-pair method with the following four thresholds for optimal performance: embedding threshold, fluctuation threshold, left- and right-histogram shrinking thresholds. 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. The left- and right-histogram shrinking thresholds are used to possibly shrink histogram from the left and right, respectively, by a certain amount for reversible data hiding. Only when all of four thresholds are satisfied the reversible data hiding is carried out. Different from our previous work, the image gray level histogram shrinking towards the center is not only for avoiding underflow and/or overflow but also for optimum performance. The required bookkeeping data are embedded together with pure payload for original image recovery. The experimental results on four popularly utilized test images (Lena, Barbara, Baboon, Airplane) and one of the JPEG2000 test image (Woman, whose histogram does not have zero points in the whole range of gray levels, and has peaks at its both ends) have demonstrated that the proposed scheme outperforms recently published reversible image data hiding schemes in terms of the highest PSNR of marked image verses original image at given pure payloads.
KW - Reversible image data hiding
KW - gray level histogram modification
KW - histogram pair scheme
KW - neighborhood fluctuation
KW - prediction error
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U2 - 10.1007/978-3-642-40099-5_31
DO - 10.1007/978-3-642-40099-5_31
M3 - Conference contribution
AN - SCOPUS:84883136316
SN - 9783642400988
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 368
EP - 383
BT - Digital Forensics and Watermaking - 11th International Workshop, IWDW 2012, Revised Selected Papers
T2 - 11th International Workshop on Digital Forensics and Watermaking, IWDW 2012
Y2 - 31 October 2012 through 3 November 2012
ER -