Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 158 countries worldwide.
Color Image Watermarking using Cycle Spinning based Sharp Frequency Localized Contourlet Transform and Principal Component Analysis
K.Kishore Kumar, Movva Pavani, V. Seshu Babu
Pages - 363 - 372     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
Color Image Watermarking, Cycle Spinning, Frequency Localization, Contourlet Transform(CT), Principal Component Analysis(PCA)
This paper describes a new approach for color image watermarking using Cycle Spinning based Sharp Frequency Localized Contourlet Transform and Principal Component Analysis. The approach starts with decomposition of images into various subbands using Contourlet Transform(CT) successively for all the color spaces of both host and watermark images. Then principal components of middle band(x bands) are considered for inserting operation. The ordinary contourlet transform suffers from lack of frequency localization. The localization being the most important criterion for watermarking, the conventional CT is not very suitable for watermarking. This problem of CT is over come by Sharp Frequency Localized Contourlet, but this lacks of translation invariance. Hence the cycle spinning based sharp frequency localized contourlet chosen for watermarking. Embedding at middle level sub bands(x band) preserves the curve nature of edges in the host image hence less disturbance is observed when host and watermark images are compared. This result in very good Peak Signal to Noise Ratio (PSNR) instead of directly adding of mid frequency components of watermark and host images the principal components are only added. Likewise the amount of payload to be added is reduced hence host images get very less distortion. Usage of principal components also helps in fruitful extraction of watermark information from host image hence gives good correlation between input watermark and extracted one. This technique has shown a very high robustness under various intentional and non intentional attacks.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 I.J. Cox, M.L. Miller, J.A. Bloom, Digital watermarking, Morgan Kaufmann, 2001.
2 A.G. Bors, I. Pitas, Image watermarking using DCT domain constraints, Proceedings of IEEE International Conference on Image Processing, vol. 3, 1996, pp. 231–234.
3 R.G.V. Schyndle, A.Z. Tirkel, C.F. Osbrone, A digital watermark, Proceedings of IEEE International Conference on Image Processing, vol. 2, 1994, pp. 86–90.
4 D. Kundur, D. Hatzinakos, Towards robust logo watermarking using multiresolution image fusion, IEEE Transcations on Multimedia 6 (2004) 185–197.
5 J. Ohnishi, K. Matsui, Embedding a seal in to a picture under orthogonal wavelet transform,Proceedings of IEEE International Conference on Multimedia and Computing system, 1996, pp.514–521, IEEE, Hiroshima, Japan.
6 P. Meerwald, A. Uhl, A survey on wavelet domain watermarking algorithms, Proceedings of SPIE, Electronic Imaging, Security and Watermarking of Multimedia Contents III, vol. 4314, 2001, pp. 505–516, SPIE, CA, USA.wise masking, IEEE Transcations on Image Processing 10 (2001)783–791.
7 Z. Dawei, C. Guanrong, L. Wenbo, A chaos based robust wavelet domain watermarking algorithm, Chaos, Solitons, and Fractals 22 (2004) 47–54.
8 A.A. Reddy, B.N. Chatterjii, A new wavelet based logo–watermarking scheme, Pattern Recognition Letters 26 (2005) 1019–1027.
9 U.Majer Ali,E.Vinoth Kumar,Digital Watermarking using DWT and SVD,NCACSA 2012 ,IJCA 2012
10 Pennec E, Mallat S, “Sparse geometric image representation with bandelets”, IEEE Transaction on Image Processing, 2005, 14(4): 423-438.
11 MN Do, M Vetterli, “The contourlet transform: an efficient directional multiresolution image representation”, IEEE Transaction on Image Processing, 2005, 14(12): 2091-2106.
12 Yue Lu, MN Do, “A New Contourlet Transform with Sharp Frequency Localization”,Proceedings of 2006 IEEE International Conference on Image Processing, IEEE, Atlanta,USA,2006, 1629-1632.
13 R. R. Coifman ,D. L. Donoho, “Translation invariant de-noising,” Wavelets and Statistics, A.Antoniadis and G. Oppenheim, Eds. New York: Springer-Verlag, 1995,125–150.
14 Eslami R , Radha H, “The contourlet transform for image denoising using cycle spinning”,Proceedings of Asilomar Conference on Signals ,Systems, and Computers, 2003,1982-1986.
Mr. K.Kishore Kumar
Faculty of Science & Technology, Icfai Foundation of Higher Education University - India
Associate Professor Movva Pavani
Aurora’s Technological & Research Institute - India
Mr. V. Seshu Babu
UURMI Systems, Hyderabad. India - India