低内存占用和高速图像小波变换

Huang Ji-jiang, Cao Jian-zhong, Yi Bo, Liu Chen
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引用次数: 0

摘要

离散小波变换(DWT)在数字图像处理中得到了广泛的应用,但对存储空间的要求和时间延迟限制了它的应用。例如,当在空间中处理图像时,需要实时处理、低功耗、降低复杂性和低内存消耗。实现基于提升的DWT是为了降低复杂性。然后将提升滤波器的系数转换为二值滤波器,从而在不使用乘法器的情况下有效地实现滤波器。这样可以提高小波变换的频率,简化结构。当进行二维小波变换时,基于线的小波变换能够在更大意义上节省内存。基于线的小波结构也可以并行执行。水平转换和垂直转换可以同时执行。本文以9/7小波为例,对其结构和性能进行了比较。这种设计的优点包括实时性能的提高、内存的减少和架构的简化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-memory-usage and high-speed image wavelet transform
Discrete wavelet transform (DWT) is being broadly used in processing digital image, but the large requirement of memory and the time delay limit the DWT usage. For instance, when the image is processed in the space, the realtime processing, low power consumption, reduced complexity and low memory consumption are required. Lifting-based DWT is implemented to lessen complexity. And then the coefficients of the lifting filters are turned to be binary and the filters are therefore implemented efficiently without using any multiplier. In this way the frequency of DWT can be improved and the architecture is simplified. When 2-dimensional DWT is carried out, the line-based wavelet transform is able to save memory in a larger sense. The architecture of line-based wavelet can also be executed in a parallel way. Both horizontal transform and vertical transform can be executed at the same time. This paper takes 9/7 wavelet as an example and compares with others its architecture and performance. The advantages of this design include real-time performance improvement, memory reduction and architecture simplification.
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