scholarly journals Performance Comparison of Wavelets Generated from Four Different Orthogonal Transforms for Watermarking With Various Attacks

2013 ◽  
Vol 9 (3) ◽  
pp. 1139-1152 ◽  
Author(s):  
H. B. Kekre ◽  
Tanuja Sarode ◽  
Shachi Natu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Image Watermarking ◽  
Histogram Equalization ◽  
Performance Comparison ◽  
Orthogonal Wavelet ◽  
Good Comparison ◽  
Orthogonal Transform ◽  
Contrast Stretching ◽  
Orthogonal Wavelet Transform

This paper proposes a watermarking technique using different orthogonal wavelet transforms like Hartley wavelet, Kekrewavelet, Slant wavelet and Real Fourier wavelet transform generated from corresponding orthogonal transform. Theseorthogonal wavelet transforms have been generated using different sizes of component orthogonal transform matrices.For example 256*256 size orthogonal wavelet transform can be generated using 128*128 and 2*2 size componentorthogonal transform. It can also be generated using 64*64 and 4*4, 32*32 and 8*8, 16*16 and 16*16 size componentorthogonal transform matrices. In this paper the focus is to compare the performance of above mentioned transformsgenerated using 128*128 and 2*2 size component orthogonal transform and 64*64 and 4*4 size component orthogonaltransform in digital image watermarking. The other two combinations are not considered as their performance iscomparatively not as good. Comparison shows that wavelet transforms generated using (128,2) combination of orthogonal transform give better performances than wavelet transforms generated using (64,4) combination of orthogonaltransformfor contrast stretching, cropping, Gaussian noise, histogram equalization and resizing attacks. Real Fourierwavelet and Slant wavelet prove to be better for histogram equalization and resizing attack respectively than DCT waveletand Walsh wavelet based watermarking presented in previous work.

scholarly journals Wavelet transforms and their applications to MHD and plasma turbulence: a review

2015 ◽  
Vol 81 (6) ◽  
Author(s):  
Marie Farge ◽  
Kai Schneider
Keyword(s):  
Numerical Simulation ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Turbulent Flows ◽  
Coherent Structures ◽  
Wavelet Transforms ◽  
Three Dimensional ◽  
Plasma Turbulence ◽  
Orthogonal Wavelet ◽  
Orthogonal Wavelet Transform

Wavelet analysis and compression tools are reviewed and different applications for the study of MHD and plasma turbulence are presented. We introduce the continuous and the orthogonal wavelet transform and detail several statistical diagnostics based on the wavelet coefficients. We then show how to extract coherent structures out of fully developed turbulent flows using wavelet-based denoising. Finally some multiscale numerical simulation schemes using wavelets are described. Several examples for analysing, compressing and computing one-, two- and three-dimensional turbulent MHD or plasma flows are presented.

scholarly journals Colour Image Watermarking using Bi-Orthogonal Wavelet Transform

2010 ◽  
Vol 11 (9) ◽  
pp. 25-29 ◽  
Author(s):  
K. Ramani ◽  
E.V. Prasad ◽  
V. Lokanadham Naidu ◽  
D. Ganesh
Keyword(s):  
Wavelet Transform ◽  
Image Watermarking ◽  
Orthogonal Wavelet ◽  
Colour Image ◽  
Orthogonal Wavelet Transform

scholarly journals SNR Improvement for Evoked Potential Estimation using Bi – Orthogonal Wavelet Transform

IJARCCE ◽  
2015 ◽  
Vol 4 (8) ◽  
pp. 408-413
Author(s):  
Shailesh M L ◽  
Dr. Anand Jatti ◽  
Madhushree K S ◽  
Siddesh M B
Keyword(s):  
Wavelet Transform ◽  
Evoked Potential ◽  
Orthogonal Wavelet ◽  
Snr Improvement ◽  
Evoked Potential Estimation ◽  
Orthogonal Wavelet Transform

Pattern Changes of Time-Shifted Vibration Signals on Wavelet Time-Scale Maps

1995 ◽  
Author(s):  
Da Jun Chen ◽  
Wei Ji Wang
Keyword(s):  
Wavelet Transform ◽  
Time Scale ◽  
Wavelet Transforms ◽  
Vibration Signal ◽  
Continuous Wavelet ◽  
Time Axis ◽  
Orthogonal Wavelet ◽  
Signal Component ◽  
Analysis Technique ◽  
Map Analysis

Abstract As a multi-resolution signal decomposition and analysis technique, the wavelet transforms have been already introduced to vibration signal processing. In this paper, a comparison on the time-scale map analysis is made between the discrete and the continuous wavelet transform. The orthogonal wavelet transform decomposes the vibration signal onto a series of orthogonal wavelet functions and the number of wavelets on one wavelet level is different from those on the other levels. Since the grids are unevenly distributed on the time-scale map, it is shown that a representation pattern of a vibration component on the map may be significantly altered or even be broken down into pieces when the signal has a shift along the time axis. On contrary, there is no such uneven distribution of grids on the continuous wavelet time-scale map, so that the representation pattern of a vibration signal component will not change its shape when the signal component shifts along the time axis. Therefore, the patterns in the continuous wavelet time-scale map are more easily recognised by human visual inspection or computerised automatic diagnosis systems. Using a Gaussian enveloped oscillation wavelet, the wavelet transform is capable of retaining the frequency meaning used in the spectral analysis, while making the interpretation of patterns on the time-scale maps easier.

Orthogonal Wavelet Transform Blind Equalization Algorithm Based on the Optimization of Artificial Fish Swarm Algorithm

2012 ◽  
Vol 198-199 ◽  
pp. 1399-1402
Author(s):  
Wei Huang ◽  
Ye Cai Guo
Keyword(s):  
Wavelet Transform ◽  
Convergence Rate ◽  
Blind Equalization ◽  
Weight Vector ◽  
Orthogonal Wavelet ◽  
Artificial Fish Swarm Algorithm ◽  
Equalization Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm ◽  
Orthogonal Wavelet Transform

According to disadvantages of big steady-state error, low convergence rate, and local convergence of traditional Constant Modulus blind equalization Algorithm (CMA), an orthogonal Wavelet Transform blind equalization Algorithm based on the optimization of Artificial Fish Swarm Algorithm(AFSA-WT-CMA) is proposed. In this proposed algorithm, the weight vector of the blind equalizer is regarded as artificial fish, the equalizer weight vector can be optimized via making full use of global search and information sharing mechanism of artificial fish school algorithm, the de-correlation ability of normalizing orthogonal wavelet transform. The computer simulations in underwater acoustic channels indicate that the proposed algorithm outperforms CMA and WT-CMA in convergence rate and mean square error.

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