Enhancing an image’s compression while keeping quality standards utilizing new mathematical technology
Abstract
The rapid development of technology led to the need to keep pace with it, especially in the field of image processing, because of its importance in the need to get better results. In this paper, new discrete Chebyshev wavelet transforms (DChWT) derived from Chebyshev polynomials (ChP) were proposed and characterized. In terms of orthogonality and approximation (convergence) in the field of mathematics, (ChP) were qualified to transform into discrete wavelets called (DChWT), depending on the mother function with operators (s, r), and were used in image processing to analyze them in the low filter and the high filter so that the image is analyzed into coefficients. Proximity and detail coefficients, which lead to dividing the image into four parts, low left (LL), in which the proximity coefficients are concentrated while the rest of the parts are centered on the detail coefficients, which are high left (HL), low right (LR), and high right (HR), and image compression through the new filter, which has proven its efficiency at level (8) in our results. The results of the proposed wavelets in this work were reached in calculating quality standards in the image obtained after the compression process after comparing them with the results obtained using the standard wavelet used in HaarSymlet 2, Conflict 2, and Daubecheis 2. The most important of these standards is a mean square error (MSE), peak signal-to-noise ratio (PSNR), bit per pixel (BPP), compression ratio (CR), and table one. In this paper, the efficiency of the proposed new wavelets is explained after adding it to MATLAB and according to a specific program to drop in with the basic wavelets to take on their role in important tasks in the image processing area, like artificial intelligence
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References
Satapathy, A., Jenila Livingston, L. M. (2016). A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud. Indian Journal of Science and Technology, 9 (6). doi: https://doi.org/10.17485/ijst/2016/v9i6/81980
Arafeen, Q. ul, Kamran, A., Arifeen, N. ul, Shaikh, A. A., Syed, N. A. (2019). Threats in the Internet of Things Pertaining to Digital Data. Proceedings of the Thirteenth International Conference on Management Science and Engineering Management, 13–29. doi: https://doi.org/10.1007/978-3-030-21248-3_2
Conti, M., Dargahi, T., Dehghantanha, A. (2018). Cyber Threat Intelligence: Challenges and Opportunities. Cyber Threat Intelligence, 1–6. doi: https://doi.org/10.1007/978-3-319-73951-9_1
Ioffe, S., Christian, S. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv. doi: https://doi.org/10.48550/arXiv.1502.03167
Kortli, Y., Jridi, M., Al Falou, A., Atri, M. (2020). Face Recognition Systems: A Survey. Sensors, 20 (2), 342. doi: https://doi.org/10.3390/s20020342
Dabhade, R. G., Waghmare, L. M. (2017). Optimal Neural Network Based Face Recognition System for Various Pose and Occluded Images. International Journal of Applied Engineering Research, 12 (22), 12625–12636. Available at: https://www.ripublication.com/ijaer17/ijaerv12n22_120.pdf
Yan, C., Xie, H., Chen, J., Zha, Z., Hao, X., Zhang, Y., Dai, Q. (2018). A Fast Uyghur Text Detector for Complex Background Images. IEEE Transactions on Multimedia, 20 (12), 3389–3398. doi: https://doi.org/10.1109/tmm.2018.2838320
Davoodi, P., Ghoreishi, S. M., Hedayati, A. (2016). Optimization of supercritical extraction of galegine from Galega officinalis L.: Neural network modeling and experimental optimization via response surface methodology. Korean Journal of Chemical Engineering, 34 (3), 854–865. doi: https://doi.org/10.1007/s11814-016-0304-2
Campbell, E., Phinyomark, A., Scheme, E. (2019). Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals. Frontiers in Neuroscience, 13. doi: https://doi.org/10.3389/fnins.2019.00437
Vishwakarma, V. P., Dalal, S. (2020). A novel non-linear modifier for adaptive illumination normalization for robust face recognition. Multimedia Tools and Applications, 79 (17-18), 11503–11529. doi: https://doi.org/10.1007/s11042-019-08537-6
Kurhe, A. B., Satonkar, S. S., Khanale, P. B., Ashok, S. (2011). Soft Computing and its applications. BIOINFO Soft Computing, 1 (1), 5–7.
Khalajzadeh, H., Mansouri, M., Teshnehlab, M. (2013). Hierarchical structure based convolutional neural network for face recognition. International Journal of Computational Intelligence and Applications, 12 (03), 1350018. doi: https://doi.org/10.1142/s1469026813500181
Redmon, J., Divvala, S., Girshick, R., Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi: https://doi.org/10.1109/cvpr.2016.91
Winarno, E., Hadikurniawati, W., Nirwanto, A. A., Abdullah, D. (2018). Multi-View Faces Detection Using Viola-Jones Method. Journal of Physics: Conference Series, 1114, 012068. doi: https://doi.org/10.1088/1742-6596/1114/1/012068
Ejaz, Md. S., Islam, Md. R., Sifatullah, M., Sarker, A. (2019). Implementation of Principal Component Analysis on Masked and Non-masked Face Recognition. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). doi: https://doi.org/10.1109/icasert.2019.8934543
Abduldaim, A. M., Abdulrahman, A. A., Tahir, F. S. (2022). The effectiveness of discrete hermite wavelet filters technique in digital image watermarking. Indonesian Journal of Electrical Engineering and Computer Science, 25 (3), 1392. doi: https://doi.org/10.11591/ijeecs.v25.i3.pp1392-1399
Mohammed, S. A., Abdulrahman, A. A., Tahir, F. S. (2022). Emotions Students’ Faces Recognition using Hybrid Deep Learning and Discrete Chebyshev Wavelet Transformations. International Journal of Mathematics and Computer Science, 17 (3), 1405–1417.
Turovsky, O., Khlaponin, Y., Hassan Mohamed, M.-A., Okhrimenko, T., Goncharenko, I., Iavich, M. (2020). Combined System of Phase Synchronization with Increased Astatism order in Frequency Monitoring Mode. CEUR Workshop Proceedings, 2616, 53–62. Available at: https://ceur-ws.org/Vol-2616/paper5.pdf
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