Volume 16 , Issue 2 , PP: 108-118, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Hanaa Mahdi Habeb 1 , Zainab Galib Salman Alrashid 2 , Saad Ismael Ibrahim 3 , Asma A. Abdulrahman 4 , Hassan Mohamed Muhi-Aldeen 5 *
Doi: https://doi.org/10.54216/JCIM.160208
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).
JPEG 2000 , Color image , Wavelet , New algorithm , Multi Resolution Analyses
[1] M. I. Razzak, S. Naz, and A. Zaib, "Deep learning for medical image processing: Overview, challenges and the future," Classification in BioApps, 2018, pp. 323–350.
[2] R. Li and W. Zhang, "Deep learning based imaging data completion for improved brain disease diagnosis," International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014, pp. 305–312.
[3] Y. A. A. S. Aldeen, M. Salleh, and M. A. Razzaque, "State of the art survey on security issue in cloud computing architectures, approaches and methods," Journal of Theoretical and Applied Information Technology, vol. 75, no. 1, pp. 53-61, 2015.
[4] E. Colleoni, S. Moccia, and X. Du, "Deep learning based robotic tool detection and articulation estimation with spatio-temporal layers," IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2714–2721, 2019.
[5] D. P. Yadav, A. Sharma, M. Singh, and A. Goyal, "Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images," IEEE Journal of Translational Engineering in Health and Medicine, vol. 7, pp. 1-7, 2019.
[6] M. Ahmed and F. Khalifa, "A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI," Indonesian Journal of Electrical Engineering and Computer Science, vol. 28, no. 2, pp. 1192–1202, 2022, doi: 10.11591/ijeecs.v28.i2.pp1192-1202.
[7] A. G. Diab and N. Fayez, "Accurate skin cancer diagnosis based on convolutional neural networks," Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 3, pp. 1429–1441, 2022, doi: 10.11591/ijeecs.v25.i3.pp1429-1441.
[8] A. Ishwarya, R. Janani, A. Nishanthi, and P. Kumar, "Kidney stone classification using Deep Neural Networks and facilitating diagnosis using IoT," International Research Journal of Engineering and Technology (IRJET), vol. 6, no. 3, pp. 8178, Mar. 2019.
[9] A. M. Al-Khafagy, S. R. Hashim, and R. A. Enad, "A unique deep-learning-based model with chest X-ray image for diagnosing COVID-19," Indonesian Journal of Electrical Engineering and Computer Science, vol. 28, no. 2, pp. 1147–1154, 2022, doi: 10.11591/ijeecs.v28.i2.pp1147-1154.
[10] S. Patel, B. Talati, S. Shah, and B. Y. Panchal, "Bone fracture classification using modified AlexNet," Stochastic Modeling & Applications, vol. 26, no. 3, pp. 1-10, Jan.-Jun. 2022.
[11] D. P. Yadav and S. Rathor, "Bone Fracture Detection and Classification using Deep Learning Approach," 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), GLA University, Mathura, UP, India, pp. 28-29, 2020.
[12] A. M. Tripathi, A. Upadhyay, A. Rajput, A. P. Singh, and B. Kumar, "Automatic detection of fracture in femur bones using image processing," in IEEE International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017, pp. 1-5.
[13] H. Y. Chai, K. L. Wee, T. T. Swee, S. H. Salleh, A. K. Ariff, and K. Kamarulafizam, "Gray-Level Co-occurrence Matrix Bone Fracture Detection," American Journal of Applied Sciences, vol. 8, pp. 26-32, 2011.
[14] Y. Ma and Y. Luo, "Bone Fracture Detection Through the Two-stage System of Crack-Sensitive Convolutional Neural Network," Journal Pre-proof, DOI: https://doi.org/10.1016/j.imu.2020.100452, 2020.
[15] J. E. Lee, Y. H. Seo, and D. W. Kim, "Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark," Applied Sciences, vol. 10, no. 19, 6854, 2020, doi: 10.3390/app10196854.
[16] A. Tavakoli, Z. Honjani, and H. Sajedi, "Convolutional Neural Network-Based Image Watermarking using Discrete Wavelet Transform," arXiv:2210.06179v1 [eess.IV], 2022, pp. 1-10.
[17] I. de Pontes Oliveira, J. L. P. Medeiros, and V. F. de Sousa, "A Data Augmentation Methodology to Improve Age Estimation using Convolutional Neural Networks," IEEE, 2016.
[18] F. Hardalaç, F. Uysal, and O. Peker, "Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models," MDPI Diagnostics, vol. 11, no. 1, pp. 1-12, 2021.
[19] S. S. Liew, M. Khalil-Hani, S. A. Radzi, and R. Bakhtieri, "Gender classification: a convolutional neural network approach," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 24, pp. 1248 - 1264, 2016, doi: 10.3906/elk-1311-58.
[20] S. Bekhet, A. M. Alghamdi, and I. Taj-Eddin, "Gender recognition from unconstrained selfie images: a convolutional neural network approach," International Journal of Electrical and Computer Engineering (IJECE), vol. 12, no. 2, pp. 2066~2078, Apr. 2022, doi: 10.11591/ijece.v12i2.pp2066-2078.
[21] A. A. Abdulrahman and F. S. Tahir, "Distinguishing license plate numbers using discrete wavelet transform technology based deep learning," Indonesian Journal of Electrical Engineering and Computer Science, vol. 30, no. 3, pp. 1771~1776, Jun. 2023, doi: 10.11591/ijeecs.v30.i3.pp1771-1776.
[22] A. A. Abdulrahman et al., "Classification of food items in trash through images using a hybrid deep learning system," Eureka: Physics and Engineering, vol. 3, pp. 145-153, 2024.
[23] S. A. P. Rosyidi, N. I. Md. Yusoff, N. F. Nadia, and M. R. Mat Yazid, "Integrated time-frequency wavelet analysis and impulse response," Ain Shams Engineering Journal, vol. 12, pp. 367–380, 2021.
[24] M. Hatoum, J. F. Couchot, R. Couturier, and R. Darazi, "Using Deep Learning for Image Watermarking Attack," HAL Id: hal-03186561, 2021, pp. 116019.
[25] H. M. Muhi-Aldeen et al., "Technology of Secure Data Exchange in the IoT System," CEUR Workshop Proc., vol. 3200, pp. 115–121, 2021.
[26] A. R. Alshahrani and M. A. Alghamdi, "Real-time object detection for autonomous vehicles using deep learning techniques," Journal of Real-Time Image Processing, vol. 20, no. 1, pp. 123–134, 2023.
[27] J. K. Smith, A. M. Jones, and L. T. Green, "An efficient deep learning framework for medical image analysis," Journal of Medical Imaging and Health Informatics, vol. 14, no. 2, pp. 345–356, 2022.
[28] F. S. Tahir, A. A. Abdulrahman, and Z. H. Thanon, "Novel face detection algorithm with a mask on neural network training," International Journal of Nonlinear Analysis and Applications, vol. 13, no. 1, pp. 209–215, 2022, doi: http://dx.doi.org/10.22075/ijnaa.2022.547.
[29] M. A. Mustafa et al., "New algorithm based on deep learning for number recognition," International Journal of Mathematics and Computer Science, vol. 18, no. 3, pp. 429–438, 2023.
[30] H. M. Muhi-Aldeen et al., "Technology of Secure Data Exchange in the IoT System," CEUR Workshop Proc., vol. 3200, pp. 115–121, 2021.
