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使用多小波和多小波包的新图像压缩技术。

New image compression techniques using multiwavelets and multiwavelet packets.

机构信息

Vision III Imaging, Herndon, VA 20170, USA.

出版信息

IEEE Trans Image Process. 2001;10(4):500-10. doi: 10.1109/83.913585.

Abstract

Advances in wavelet transforms and quantization methods have produced algorithms capable of surpassing the existing image compression standards like the Joint Photographic Experts Group (JPEG) algorithm. For best performance in image compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry. However, the design possibilities for wavelets are limited because they cannot simultaneously possess all of the desirable properties. The relatively new field of multiwavelets shows promise in obviating some of the limitations of wavelets. Multiwavelets offer more design options and are able to combine several desirable transform features. The few previously published results of multiwavelet-based image compression have mostly fallen short of the performance enjoyed by the current wavelet algorithms. This paper presents new multiwavelet transform and quantization methods and introduces multiwavelet packets. Extensive experimental results demonstrate that our techniques exhibit performance equal to, or in several cases superior to, the current wavelet filters.

摘要

小波变换和量化方法的进步已经产生了能够超越现有图像压缩标准(如联合图像专家组(JPEG)算法)的算法。为了在图像压缩中获得最佳性能,小波变换需要滤波器,这些滤波器结合了一些理想的特性,如正交性和对称性。然而,由于小波不能同时具有所有理想的特性,因此它们的设计可能性受到限制。相对较新的多小波领域有望克服小波的一些限制。多小波提供了更多的设计选择,并能够组合几个理想的变换特性。之前发表的少数基于多小波的图像压缩结果在性能上大多不如当前的小波算法。本文提出了新的多小波变换和量化方法,并介绍了多小波包。大量的实验结果表明,我们的技术表现与当前的小波滤波器相当,在某些情况下甚至优于当前的小波滤波器。

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