改进分数阶小波变换的FPGA实现

J. Abdul-Jabbar, Sara W. Abboodi
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引用次数: 1

摘要

鲁棒通信和图像网络传输的源编码通常需要小波算法的应用。尽管由于廉价的CMOS相机的可用性,无线多媒体传感器被广泛用于传输多媒体内容,但它们的计算和内存资源通常仍然非常有限。众所周知,允许具有有限RAM大小的低成本相机传感器节点执行多级小波变换,将反过来限制所获取图像的大小。近年来,分数小波滤波技术成为一种有趣的解决方案,以减少通信负担和无线带宽,为资源有限的设备。传统的分数阶小波变换以牺牲重构图像的质量为代价,大大减少了所需的内存。本文提出了两种新的改进分数阶小波变换,以减少先前分数阶小波变换引起的边界伪影。在第一种提出的技术中,获得任意两个连续分数的最后一行和第一行的平均值以及最后一行和第二行之前的平均值,分别位于相同分数的同一行。在第二种提出的技术中,获得任意两个连续分数的最后一行和第一行的平均值,然后分别位于相同分数的同一行。利用Haar和bio 5/3小波滤波器对不同类型、不同尺寸的图像进行重构,结果表明该方法具有良好的应用前景。这些技术的FPGA实现表明后者也是优越的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FPGA implementation of modified fractional wavelet transforms
Source coding of robust communication and network transmission of images usually needs the applications of wavelet-based algorithms. Although wireless multimedia sensors are widely used to deliver multimedia content due to the availability of inexpensive CMOS cameras, their computational and memory resources are still typically very limited. It is known that allowing a low-cost camera sensor node with limited RAM size to perform a multi-level wavelet transform, will in turn limit the size of the acquired image. Recently, fractional wavelet filters technique became an interesting solution to reduce communication burden and wireless bandwidth, for resource-constrained devices. Classical fractional wavelet transforms can achieve much reduction in the required memory at the expense of the reconstructed image quality. In this paper, two new modified fractional wavelet transforms are proposed to reduce boundaries artifacts caused by the previous fractional wavelets. In the first proposed technique, the average of the last and the first rows and the average of the row before the last one and the second row of any two successive fractions are obtained and, respectively located at the same rows of the same fractions. In the second proposed technique, the average of the last and the first rows of any two successive fractions is obtained and then, respectively located at the same rows of the same fractions. The resulting reconstructed images of the later technique highlight the promising performance of its applications on different types of images with different sizes using Haar and bio 5/3 wavelet filters. FPGA implementations of such techniques indicate that the later one is also superior.
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