Study of High Performance Information Hiding Algorithm Based on CL multi-wavelet Transform and Color Field Structure Analysis Process

2012 ◽  
Vol 4 (14) ◽  
pp. 46-52
Author(s):  
Shuai REN ◽  
Tao ZHANG ◽  
Dejun MU
Keyword(s):  
Wavelet Transform ◽  
Structure Analysis ◽  
Information Hiding ◽  
High Performance ◽  
Field Structure ◽  
Performance Information ◽  
Analysis Process ◽  
Color Field

Study of Reversible Information Hiding Scheme Based on CARDBAL2 and Color Field Structure

2011 ◽  
Vol 225-226 ◽  
pp. 275-279
Author(s):  
Shuai Ren ◽  
Tao Zhang
Keyword(s):  
Image Processing ◽  
Information Hiding ◽  
Chaotic Map ◽  
Field Structure ◽  
Change Of Direction ◽  
First Order ◽  
Maximum Change ◽  
Reversible Information Hiding ◽  
Color Field ◽  
Knight’S Tour

Propose a new image processing method which named Color Field Structure (CFS). The direction of CFS represents the information. Take advantage of the feature that the energy of the image would gather and spread on four components (LL2, LH2, HL2 and HH2) in the sub-image after first-order CARDBAL2 multi-wavelet transform. Propose an Information Hiding Algorithm based on modifying the direction of CFS in four CARDBAL2 first-order sub-images. The maximum change of direction angle is usually less than π/2k. The information is divided into two parts, one part is embedded in LL2 and HH2, and the other part is embedded in LH2 and HL2. Use the Logistic chaotic map, Knight's Tour traversal and the genetic algorithm to improve the consistence of the embedded data bits’ order and the character of the sub-image. Experimental results indicate that the proposed scheme can increase invisibility and robustness separately by 28.64% and 23.06% averagely, and has certain ability against steganalysis such as Higher Order Statistics based on wavelet coefficients. Moreover, the scheme has excellent sensitivity of image processing.

scholarly journals Soft Error Susceptibility Analysis of SRAM-Based FPGAs in High-Performance Information Systems

2007 ◽  
Vol 54 (6) ◽  
pp. 2714-2726 ◽  
Author(s):  
Hossein Asadi ◽  
Mehdi B. Tahoori ◽  
Brian Mullins ◽  
David Kaeli ◽  
Kevin Granlund
Keyword(s):  
Information Systems ◽  
High Performance ◽  
Soft Error ◽  
Performance Information ◽  
Susceptibility Analysis

Wavelet Transform Based Feature Extraction for EEG Signal Classification

2021 ◽  
Vol 20 ◽  
pp. 199-206
Author(s):  
Seda Postalcioglu
Keyword(s):  
Wavelet Transform ◽  
High Performance ◽  
Feature Vector ◽  
Fast Response ◽  
Eeg Signal ◽  
Real Time Control ◽  
Time Control ◽  
Band Power ◽  
Performance Rate

This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds

Enhanced Three-Dimensional Color Conversion Based on Discrete Wavelet Transform

2012 ◽  
Vol 155-156 ◽  
pp. 440-444
Author(s):  
He Yan ◽  
Xiu Feng Wang
Keyword(s):  
Information Technology ◽  
Wavelet Transform ◽  
Image Reconstruction ◽  
Image Quality ◽  
Image Compression ◽  
High Performance ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Compression Algorithms ◽  
Color Conversion

JPEG2000 algorithm has been developed based on the DWT techniques, which have shown how the results achieved in different areas in information technology can be applied to enhance the performance. Lossy image compression algorithms sacrifice perfect image reconstruction in favor of decreased storage requirements. Wavelets have become a popular technology for information redistribution for high-performance image compression algorithms. Lossy compression algorithms sacrifice perfect image reconstruction in favor of improved compression rates while minimizing image quality lossy.

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