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Register a new userمقارنة بين تحويل الحواف و التحويل المويجي المتقطع و التحويل التجيبي المتقطع في تطبيق العلامة المائية على الصور الرقمية
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العلامة المائية هي عبارة عن إشارة يتم تضمنيها ضمن البيانات الرقمية (نص ، صورة ، صوت ، فيديو ) بطريقة تمكن من استخراجها لاحقاً ، و غالباً ما تستخدم لتضمين معلومات حقوق الملكية الفكرية للبيانات ، و يتم ذلك من خلال تضمين نموذج ما يحوي بيانات المؤلف ضمن البيانات الرقمية. نقدم في هذا البحث مقارنة بين ثلاثة أنواع من التحويلات المستخدمة لتضمين العلامة المائية ضمن المجال الترددي بطريقة فعالة و آمنة تسمح بتعليم جميع الصور الرقمية بدقة.
A digital watermark is a signal that is embedded into digital data (text, image, audio, video) in a manner that allows it to be extracted later. This is done by embedding a pattern which contains the author's data into the digital data. In this research, we propose a comparison between three types of transformations for embedding a watermark in the frequency domain into digital images in an efficient and secure method that allows the watermarking any type of digital images with good perceptibility.
References used
ALSAIF K and ABDULLAH A, 2013- "Contourlet Transform and Histogram Equalization for Brightness Enhancement of Color Image". International Journal of Computer Networks and Communications Security, Vol. 1, No. 4,140-143
BABU S and RAJESH V and NAVAYA L and BHAVYASR G, 2013- "The Contourlet Transform For The Application In Image Denoising".International Journal Of Systems And Technologies, Vol. 6, No. 1, 38-48
CHETRI C and DESAI S, 2014- "Review Of Imperceptible Techniques For Still Digital Image Watermarking". International Journal of Advanced Research in Computer Engineering & Technolo, Vol. 3, No. 2, 389-395
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In this paper, we propose a new method to embed digital
watermarking in audio files, using Discrete Wavelet Transform
(DWT) and the way to extract the watermark data.
The method efficiency is measured using Peak Signal –to-Noise
Ratio (PSNR) , No
rmalized Correlation Coefficient (NC). The
advantage of our method is the robustness against several attacks
and compression.
دراسة وتحليل أداء نظام التجميع بالتقسيم الترددي المتعامد باستخدام التحويل المويجي المتقطع مع السلاسل الطورية
Epilepsy is a chronic neurological disorder that occurs in the brain،
and affects approximately 2% of people around the world، where
epilepsy patients face a lot of difficulties in everyday life due to the
occurrence of seizures. Electroencephalog
ram (EEG) is used in
the automated detection of epileptic seizures، which has
Characteristics of non-linear and non-stationary. In this research،
we conducted automated detection of the seizures from the scalp
EEG signals using a Level 5 Discrete Wavelet Transforms DWT to
analyze the signal and extracting statistical features (maximum،
minimum، mean، average ، standard deviation، the ratio between
the mean values) and Categorizing using artificial neural networks
ANN for classification. The suggested detection method has
89.85% detection accuracy with 90.60% sensitivity ، and 89.1%
specificity.
In our research, we used a video file as a cover media to
hide the text. This method provides a high quality in
display and authenticity in data transfer.
We chose specific frames from the video file and we apply
DWT to hide the encryption text, which ensure that the text
file is delivered correctly and secretly.
Image compression is one of the most important branches of digital image
processing. It reduces the size of the captured images and minimizes the storage space on
the drivers to speed up the transferring and transmission.
In this paper we will pre
sent a new approach for compressing stereo images based on
three algorithms; the first one is comparing the two images that perform the stereoscopic
view by noticing the great similarities between them and encoding the difference between
the two images instead of encoding the whole image. The second one is reducing the
redundancy between the Pixels using a 2D Digital Curvelet Transformation so we can
utilize the great ability to represent the curves in the image with minimum number of
coefficients. Then quantize them and remove undesirable coefficient. The low number of
coefficient contains most of image data. Last one is using Huffman Encoding and take
advantage of the lossless property so we can encode image and reduce the size of data
without getting any image distortion or lose any part of this image.
The performance of the proposed algorithm evaluated using Compression Ratio
standard which is the number of the image bits after compression to the number of the
original image bits before compression. Also, Peak Signal to Noise Ratio standard (PSNR)
which represent the similarity between the restored image and the original image. In final,
the Mean Square Error standard (MSE) which represent the error between the restored
image and original image.
In conclusion, the main objective here is to get the lowest rate for image compression
ratio with the highest value for the image quality PSNR at the lowest value of the errors
MSE.
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