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A Parallel Algorithm for Wavelet Transform-Based Color Image Compression

机译:基于小波变换的彩色图像压缩的并行算法

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摘要

Wavelet transforms emerge as one of the popular techniques in image compression. This technique is accepted by the JPEG Committee for the next-generation image compression standard JPEG-2000. Convolution-based strategy is widely used in calculating the wavelet transform of the image. A convolution-based wavelet transform consists of a large number of multiplications and additions. A color image consists of a two-dimensional matrix each for red, green, and blue colors. An ordinary way to calculate the wavelet transform of a color image includes calculating the transform of the intensity matrix of the red, green, and blue components. In this article, we present a parallel algorithm for calculating the convolution-based wavelet transform of the red, green, and blue intensity components simultaneously in color images, which can run on commonly used processors. This means that it needs no extra hardware. The results are also compared to the nonparallel algorithm based on compression time, mean square error, compression ratio, and peak signal-to-noise ratio. Complexity analysis and comparative complexity analysis with some other papers are also shown here.
机译:小波变换作为图像压缩中的流行技术之一。 JPEG委员会接受该技术,用于下一代图像压缩标准JPEG-2000。基于卷积的策略广泛用于计算图像的小波变换。基于卷积的小波变换包括大量乘法和添加。彩色图像由两个维矩阵组成,每个矩阵为红色,绿色和蓝色。计算彩色图像的小波变换的普通方式包括计算红色,绿色和蓝色组件的强度矩阵的变换。在本文中,我们提出了一种并行算法,用于在彩色图像中同时计算红色,绿色和蓝色强度分量的基于卷积的小波变换,这可以在常用的处理器上运行。这意味着它不需要额外的硬件。结果也将与基于压缩时间,均方误差,压缩比和峰值信噪比的非平行算法进行比较。这里还显示了与其他一些论文的复杂性分析和比较复杂性分析。

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