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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Song, C; Sudirman, S; Merabti, M
Publisher: http://www.cms.livjm.ac.uk/pgnet2011/Proceedings/Papers/m1569452837-song.pdf
Languages: English
Types: Unknown
Subjects: QA75

Classified by OpenAIRE into

ACM Ref: Data_MISCELLANEOUS
Improving the robustness of watermark in withstanding attacks has been one of the main research objectives in digital image watermarking. In this paper we propose a novel region-adaptive watermarking technique that can provide improvements in both robustness and visual quality of the watermarks when compared to the original, non-region-adaptive, embedding technique. The proposed technique, which is derived from our previously published research finding, shows that the relative difference in spectral distributions between the watermark data and the host image plays an important role in improving the watermark robustness and transparency.\ud \ud Keywords – Quad-tree algorithm, discrete wavelet transform, region-based watermarking system.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • [3] Cl.Song, S.Sudirman and M.Merabti, ―A Spatial and Frequency Domain Analysis of the Effect of Removal Attacks on Digital Image Watermarks‖, Proc 11th of PostGraduate Network Symposium, 119-124, June, 2010.
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    • [5] P.N.Tao and Eskicioglu, ―A robust multiple watermarking scheme in the Discrete Wavelet Transform domain‖, Internet Multimedia Management System Conference, vol. 5601,133-144, 2004.
    • [6] Chad, C., Magan, T., Serge, B., Joseph H., Jitendra M.: Blobworld: A System for Region-Based Image Indexing and Retrieval, Visuan Information and Inforamtion Systems, Lecture Notes in Computer Science, Vol: 1614/1999, 1999.
    • [7] Jain, A.K., Farrokhnia, F.: Unsupervised Texture Segmentation Using Gabor Filters, Systems, Man and Cybernetics, Conference Proceedings., 14-19, Nov, 1990
    • [8] Erdogan, C., Deliang, W,: Texture Segmentation Using GaussianMarkov Random Field and Neural Oscillator Networks, IEEE Transactions on Neural Networks, Vol. 12, No. 2, 394-404, March 2001.
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