Lossless data embedding using generalized statistical quantity histogram

Gao, X; An, L; Yuan, Y; Tao, D; Li, X

Lossless data embedding using generalized statistical quantity histogram

Gao, X An, L Yuan, Y Tao, D

Li, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21 (8), pp. 1061 - 1070
Issue Date:: 2011-08-01

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Field	Value	Language
dc.contributor.author	Gao, X	en_US
dc.contributor.author	An, L	en_US
dc.contributor.author	Yuan, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2011-08-01	en_US
dc.identifier.citation	IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21 (8), pp. 1061 - 1070	en_US
dc.identifier.issn	1051-8215	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18250
dc.description.abstract	Histogram-based lossless data embedding (LDE) has been recognized as an effective and efficient way for copyright protection of multimedia. Recently, a LDE method using the statistical quantity histogram has achieved good performance, which utilizes the similarity of the arithmetic average of difference histogram (AADH) to reduce the diversity of images and ensure the stable performance of LDE. However, this method is strongly dependent on some assumptions, which limits its applications in practice. In addition, the capacities of the images with the flat AADH, e.g., texture images, are a little bit low. For this purpose, we develop a novel framework for LDE by incorporating the merits from the generalized statistical quantity histogram (GSQH) and the histogram-based embedding. Algorithmically, we design the GSQH driven LDE framework carefully so that it: 1) utilizes the similarity and sparsity of GSQH to construct an efficient embedding carrier, leading to a general and stable framework; 2) is widely adaptable for different kinds of images, due to the usage of the divide-and-conquer strategy; 3) is scalable for different capacity requirements and avoids the capacity problems caused by the flat histogram distribution; 4) is conditionally robust against JPEG compression under a suitable scale factor; and 5) is secure for copyright protection because of the safe storage and transmission of side information. Thorough experiments over three kinds of images demonstrate the effectiveness of the proposed framework. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Circuits and Systems for Video Technology	en_US
dc.relation.isbasedon	10.1109/TCSVT.2011.2130410	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Lossless data embedding using generalized statistical quantity histogram	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	21	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	21	en_US

Abstract:

Histogram-based lossless data embedding (LDE) has been recognized as an effective and efficient way for copyright protection of multimedia. Recently, a LDE method using the statistical quantity histogram has achieved good performance, which utilizes the similarity of the arithmetic average of difference histogram (AADH) to reduce the diversity of images and ensure the stable performance of LDE. However, this method is strongly dependent on some assumptions, which limits its applications in practice. In addition, the capacities of the images with the flat AADH, e.g., texture images, are a little bit low. For this purpose, we develop a novel framework for LDE by incorporating the merits from the generalized statistical quantity histogram (GSQH) and the histogram-based embedding. Algorithmically, we design the GSQH driven LDE framework carefully so that it: 1) utilizes the similarity and sparsity of GSQH to construct an efficient embedding carrier, leading to a general and stable framework; 2) is widely adaptable for different kinds of images, due to the usage of the divide-and-conquer strategy; 3) is scalable for different capacity requirements and avoids the capacity problems caused by the flat histogram distribution; 4) is conditionally robust against JPEG compression under a suitable scale factor; and 5) is secure for copyright protection because of the safe storage and transmission of side information. Thorough experiments over three kinds of images demonstrate the effectiveness of the proposed framework. © 2011 IEEE.

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http://hdl.handle.net/10453/18250