增强图像的压缩,同时保持质量标准,利用新的数学技术

Q3 Engineering
A. Abdulrahman, J. A. Eleiwy, Ibtehal Shakir Mahmoud, Hassan Mohamed Muhi-Aldeen, F. S. Tahir, Y. Khlaponin
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引用次数: 0

摘要

技术的飞速发展导致需要与之保持同步,特别是在图像处理领域,因为它的重要性需要得到更好的结果。本文提出了一种新的基于Chebyshev多项式的离散Chebyshev小波变换(DChWT),并对其进行了表征。根据数学领域的正交性和逼近性(收敛性),(ChP)可以根据母函数和算子(s, r)变换成离散小波(DChWT),并用于图像处理,在低滤波器和高滤波器中对其进行分析,从而将图像分析成系数。接近度和细节系数,将图像划分为四个部分,左下部分(LL),其中接近系数集中,其余部分集中在细节系数上,即左上部分(HL),右下部分(LR)和右上部分(HR),通过新的滤波器进行图像压缩,在我们的结果中证明了它在级别(8)上的效率。将本文提出的小波与HaarSymlet 2、Conflict 2和Daubecheis 2中使用的标准小波的结果进行比较,得出了计算压缩后图像质量标准的结果。这些标准中最重要的是均方误差(MSE)、峰值信噪比(PSNR)、每像素比特数(BPP)、压缩比(CR)和表1。本文将提出的新小波添加到MATLAB中,并根据具体的程序将其与基本小波结合,在图像处理领域的重要任务中发挥作用,如人工智能
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing an image’s compression while keeping quality standards utilizing new mathematical technology
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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来源期刊
EUREKA: Physics and Engineering
EUREKA: Physics and Engineering Engineering-Engineering (all)
CiteScore
1.90
自引率
0.00%
发文量
78
审稿时长
12 weeks
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