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Analysis of Wavelets with Watermarking through Wavelet Transformation

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 22
Year of Publication: 2014
Authors:
Manupriya
Tarun Kumar
10.5120/16926-6939

Manupriya and Tarun Kumar. Article: Analysis of Wavelets with Watermarking through Wavelet Transformation. International Journal of Computer Applications 96(22):17-22, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Manupriya and Tarun Kumar},
	title = {Article: Analysis of Wavelets with Watermarking through Wavelet Transformation},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {22},
	pages = {17-22},
	month = {June},
	note = {Full text available}
}

Abstract

Digital Watermarking has become essential in today's scenario because lots of digital data has become distributed all over the internet. The access to internet has become very simple and inexpensive for users in the past 15 years due to the rapid technological advancement. Therefore the possibility for the various digital attacks like forgery, damaging the digital data, stealing, copying and alteration of the digital information has also increased. In such situations, it has become significant to use a protecting mechanism that can protect the digital data and its copyright protection of the owner of digital data. Digital watermarking is a very efficient solution to this problem. Digital watermarking is a technique that embeds a watermark in the host digital data that stores the copyright information about the data. The proposed watermarking scheme is implemented in frequency domain using wavelet transform. In this paper, db wavelets are analyzed for the watermarking schemes. There are 45 db wavelets, each of them is analyzed with respect to watermarking scheme and resultant db wavelets are given as the output which wavelets are supporting the watermarking scheme. Each of the resultant wavelet is tested against the quality parameter such as Mean Square Error, Maximum Difference, and Normalized Cross Correlation, Structural Count, and Normalized Absolute Error and Peak-Signal-To-Noise ratio.

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