Fuzzy Transform Image Compression in the YUV Space

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Barbara Cardone, Ferdinando Di Martino, Salvatore Sessa
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引用次数: 0

Abstract

This research proposes a new image compression method based on the F1-transform which improves the quality of the reconstructed image without increasing the coding/decoding CPU time. The advantage of compressing color images in the YUV space is due to the fact that while the three bands Red, Green and Blue are equally perceived by the human eye, in YUV space most of the image information perceived by the human eye is contained in the Y band, as opposed to the U and V bands. Using this advantage, we construct a new color image compression algorithm based on F1-transform in which the image compression is accomplished in the YUV space, so that better-quality compressed images can be obtained without increasing the execution time. The results of tests performed on a set of color images show that our color image compression method improves the quality of the decoded images with respect to the image compression algorithms JPEG, F1-transform on the RGB color space and F-transform on the YUV color space, regardless of the selected compression rate and with comparable CPU times.
YUV空间中的模糊变换图像压缩
本研究提出了一种新的基于f1变换的图像压缩方法,在不增加编码/解码CPU时间的前提下,提高了重构图像的质量。在YUV空间压缩彩色图像的优势在于人眼可以同样感知红、绿、蓝三个波段,而人眼感知到的大部分图像信息在YUV空间中包含在Y波段,而不是U和V波段。利用这一优势,我们构建了一种新的基于f1变换的彩色图像压缩算法,该算法在YUV空间完成图像压缩,在不增加执行时间的情况下获得质量更好的压缩图像。在一组彩色图像上进行的测试结果表明,无论所选择的压缩率如何,在CPU时间相当的情况下,与图像压缩算法JPEG、RGB颜色空间的f1变换和YUV颜色空间的f变换相比,我们的彩色图像压缩方法提高了解码图像的质量。
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来源期刊
Computation
Computation Mathematics-Applied Mathematics
CiteScore
3.50
自引率
4.50%
发文量
201
审稿时长
8 weeks
期刊介绍: Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.
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