Robust lossless data hiding using clustering and statistical quantity histogram

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Robust lossless data hiding using clustering and statistical quantity histogram
	An, Lingling 2; Gao, Xinbo 2,3; Yuan, Yuan1 ; Tao, Dacheng 4
刊名	neurocomputing
	2012-02-01
卷号	77 期号:1 页码:1-11
关键词	Just noticeable distortion k-Means clustering Robust lossless data hiding Statistical quantity histogram
ISSN号	0925-2312
产权排序	2
合作状况	国际
中文摘要	lossless data hiding methods usually fail to recover the hidden messages completely when the watermarked images are attacked. therefore, the robust lossless data hiding (rldh), or the robust reversible watermarking technique, is urgently needed to effectively improve the recovery performance. to date a couple of methods have been developed; however, they have such drawbacks as poor visual quality and low capacity. to solve this problem, we develop a novel statistical quantity histogram shifting and clustering-based rldh method or sqh-sc for short. the benefits of sqh-sc in comparison with existing typical methods include: (1) strong robustness against lossy compression and random noise due to the usage of k-means clustering; (2) good imperceptibility and reasonable performance tradeoff due to the consideration of the just noticeable distortion of images; (3) high capacity due to the flexible adjustment of the threshold; and (4) wide adaptability and good stability to different kinds of images. extensive experimental studies based on natural images, medical images, and synthetic aperture radar (sar) images demonstrate the effectiveness of the proposed sqh-sc.
英文摘要	lossless data hiding methods usually fail to recover the hidden messages completely when the watermarked images are attacked. therefore, the robust lossless data hiding (rldh), or the robust reversible watermarking technique, is urgently needed to effectively improve the recovery performance. to date a couple of methods have been developed; however, they have such drawbacks as poor visual quality and low capacity. to solve this problem, we develop a novel statistical quantity histogram shifting and clustering-based rldh method or sqh-sc for short. the benefits of sqh-sc in comparison with existing typical methods include: (1) strong robustness against lossy compression and random noise due to the usage of k-means clustering; (2) good imperceptibility and reasonable performance tradeoff due to the consideration of the just noticeable distortion of images; (3) high capacity due to the flexible adjustment of the threshold; and (4) wide adaptability and good stability to different kinds of images. extensive experimental studies based on natural images, medical images, and synthetic aperture radar (sar) images demonstrate the effectiveness of the proposed sqh-sc. (c) 2011 published by elsevier b.v.
学科主题	computer science ; artificial intelligence
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence
研究领域[WOS]	computer science
关键词[WOS]	digital watermarking ; image watermarking
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000298206400001
公开日期	2012-09-03
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/20245]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 2.Xidian Univ, VIPS Lab, Sch Elect Engn, Xian 710071, Peoples R China 3.Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Minist Educ China, Xian 710071, Peoples R China 4.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Broadway, NSW 2007, Australia
推荐引用方式 GB/T 7714	An, Lingling,Gao, Xinbo,Yuan, Yuan,et al. Robust lossless data hiding using clustering and statistical quantity histogram[J]. neurocomputing,2012,77(1):1-11.
APA	An, Lingling,Gao, Xinbo,Yuan, Yuan,&Tao, Dacheng.(2012).Robust lossless data hiding using clustering and statistical quantity histogram.neurocomputing,77(1),1-11.
MLA	An, Lingling,et al."Robust lossless data hiding using clustering and statistical quantity histogram".neurocomputing 77.1(2012):1-11.