CLC number: TP391.41
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-10-17
Cited: 0
Clicked: 6900
Citations: Bibtex RefMan EndNote GB/T7714
M. F. Kazemi, M. A. Pourmina, A. H. Mazinan. Level-direction decomposition analysis with a focus on image watermarking framework[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1199-1217.
@article{title="Level-direction decomposition analysis with a focus on image watermarking framework",
author="M. F. Kazemi, M. A. Pourmina, A. H. Mazinan",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="11",
pages="1199-1217",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500165"
}
%0 Journal Article
%T Level-direction decomposition analysis with a focus on image watermarking framework
%A M. F. Kazemi
%A M. A. Pourmina
%A A. H. Mazinan
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 11
%P 1199-1217
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500165
TY - JOUR
T1 - Level-direction decomposition analysis with a focus on image watermarking framework
A1 - M. F. Kazemi
A1 - M. A. Pourmina
A1 - A. H. Mazinan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 11
SP - 1199
EP - 1217
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500165
Abstract: This research addresses the new level-direction decomposition in the area of image watermarking as the further development of investigations. The main process of realizing a watermarking framework is to generate a watermarked image with a focus on contourlet embedding representation. The approach performance is evaluated through several indices including the peak signal-to-noise ratio and structural similarity, whereby a set of attacks are carried out using a module of simulated attacks. The obtained information is analyzed through a set of images, using different color models, to enable the calculation of normal correlation. The module of the inverse of contourlet embedding representation is correspondingly employed to obtain the present watermarked image, as long as a number of original images are applied to a scrambling module, to represent the information in disorder. This allows us to evaluate the performance of the proposed approach by analyzing a complicated system, where a decision making system is designed to find the best level and the corresponding direction regarding contourlet embedding representation. The results are illustrated in appropriate level-direction decomposition. The key contribution lies in using a new integration of a set of subsystems, employed based upon the novel mechanism in contourlet embedding representation, in association with the decision making system. The presented approach is efficient compared with state-of-the-art approaches, under a number of serious attacks. A number of benchmarks are obtained and considered along with the proposed framework outcomes. The results support our ideas.
The article presented watermarking in contourlet transform.
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