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Reseach Article

A Comparative performance evaluation of SVD and Schur Decompositions for Image Watermarking

Published on None 2011 by B.Chandra Mohan, K.Veera Swamy, S.Srinivas Kumar
International Conference on VLSI, Communication & Instrumentation
Foundation of Computer Science USA
ICVCI - Number 14
None 2011
Authors: B.Chandra Mohan, K.Veera Swamy, S.Srinivas Kumar
e2f284d1-efee-4fba-8574-c9961743c908

B.Chandra Mohan, K.Veera Swamy, S.Srinivas Kumar . A Comparative performance evaluation of SVD and Schur Decompositions for Image Watermarking. International Conference on VLSI, Communication & Instrumentation. ICVCI, 14 (None 2011), 25-29.

@article{
author = { B.Chandra Mohan, K.Veera Swamy, S.Srinivas Kumar },
title = { A Comparative performance evaluation of SVD and Schur Decompositions for Image Watermarking },
journal = { International Conference on VLSI, Communication & Instrumentation },
issue_date = { None 2011 },
volume = { ICVCI },
number = { 14 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 25-29 },
numpages = 5,
url = { /proceedings/icvci/number14/2735-1529/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on VLSI, Communication & Instrumentation
%A B.Chandra Mohan
%A K.Veera Swamy
%A S.Srinivas Kumar
%T A Comparative performance evaluation of SVD and Schur Decompositions for Image Watermarking
%J International Conference on VLSI, Communication & Instrumentation
%@ 0975-8887
%V ICVCI
%N 14
%P 25-29
%D 2011
%I International Journal of Computer Applications
Abstract

In this paper, the performance of SVD and Schur decomposition is evaluated and compared for image copyright protection applications. The watermark image is embedded in the cover image by using Quantization Index Modulus Modulation (QIMM) and Quantization Index Modulation (QIM). Watermark image is embedded in the D matrix of Schur decomposition and Singular Value Decomposition (SVD). Watermarking in SVD domain is highly flexible . This is due to the availability of three matrices for watermarking. Singular values in SVD and Schur decomposition are highly stable. Compared to Singular Value Decomposition (SVD), Schur decomposition is computationally faster and robust to image attacks. The proposed algorithms based on SVD and Schur decompositions are more secure and robust to various attacks, viz., rotation, low pass filtering, median filtering, resizing, salt & pepper noise. Superior experimental results are observed with the proposed algorithm over a recent scheme proposed by Chung et al. in terms of Normalized Cross correlation (NCC) and Peak Signal to Noise Ratio (PSNR).

References
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Index Terms

Computer Science
Information Sciences

Keywords

Digital Image Watermarking Schur Decomposition SVD PSNR NCC