Denoising and Compressing Color Images Using New Wavelet Efficiency
Hanaa Mahdi Habeb1, Zainab Galib Salman Alrashid2, Saad Ismael Ibrahim3, Asma A. Abdulrahman4, Hassan Mohamed Muhi-Aldeen5,*
1Department of computer science, University of Technology, 52 Alsena str., Baghdad, 10053, Iraq
2Aliraqia University, 22Aladamia, Baghdad, Iraq
3College of Media- Unit of Rehabilitation Employment and Fallow Up, Al-Iraqia University, Iraq
4Department of Applied Sciences, University of Technology, 52 Alsena str., Baghdad, 10053, Iraq
5Department of Computer Engineering, Aliraqia University, 22Sabaabkar, Adamia, Baghdad, Iraq
Emails: hanaa.m.habeb@uotechnology.edu.iq; zainab_alrashid@aliraqia.edu.iq; saad.i.ibrahim@aliraqia.edu.iq; asma.a.abdulrahman@uotechnology.edu.iq; muhialdeen.hassan@aliraqia.edu.iq
Abstract
Compressing color images (such as JPEG 2000) with wavelet transforms that are used in image parsing into approximate and detailed coefficients in the Multi Resolution Analyses (MRA) stage, such as Symlet 2, Coiflet 2, and Daubechies 2. The rapid development that occurs in modern life and the development of technology and artificial intelligence has increased the need to find an advanced and fast technology in image transfer, which requires reducing the space used by very large image data through the compression process that images need during transfer and transmission. Therefore, the need to accomplish this work has been necessitated by finding a new method and purely mathematical methods with equations and transformations that will be performed on Hermite polynomials to obtain the discrete Hermite wavelets (DHWT) to meet the great challenge in the field of images due to the mathematical properties that characterize these waves to be ready to perform the image analysis process known in the field of images (MRA), which is summarized in entering the color image to analyze the color image into two types of coefficients, which are detail coefficients and convergence coefficients due to the high level and low level, respectively, to divide the image into four blocks, which are Low Low, High Low, Low High and High High to then remove the noise and then compress, A suitable algorithm was created in MATLAB to read the program for this tool as in common waves (Symlet 2, Coiflet 2, and Daubechies 2) to obtain good results with new wavelet. The results obtained and through comparisons with basic wavelet work such as Haar and Daubechies etc. to obtain the values of the most important image quality parameters and the experiment was carried out on a sample of JPEG 2000 The tables in this work show the results that will be obtained that prove the efficiency of the proposed model after calculating the image quality parameters Mean Square Error (MSE), Peak Signal of Noise Ratio (PSNR), Bit Per Pixel (BPP) and Compassion Ratio (CR).
Keywords: JPEG 2000; Color image; Wavelet; New algorithm; Multi Resolution Analyses