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2-D DWT system architecture for image compression

机译:用于图像压缩的二维DWT系统架构

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摘要

Since the introduction of wavelet transform in early 1980s, it has contributed significantly in multiple areas, such as image processing and compression, radar signal analysis, numerical analysis, biomedical signal processing, medical imaging and digital signal processing. The key advantage of wavelet analysis is the extra time and frequency information compared to other transforms. However, the discrete wavelet transform (DWT) requires very large memory requirement and is computationally intensive, especially for 2-D transform. Typically, it has quadratic computational complexity. In this project, we propose a fully dedicated processor which specialized in 2-D DWT. This architecture aims to achieve improvements on throughput, scalability and flexibility compared to other prior architectures. This architecture requires significantly less computational resources and internal memory. The proposed architecture can achieve threoritical throughput of 138fps for a 2048x1566 video processing. The DWT system has been designed for scalability by supporting up to 8 parallel DWT engines and each DWT engine can work independently. The DWT system architecture is very flexible and the performance can be scaled by increasing or reducing the DWT engines, according to different application needs. Furthermore, this architecture has been designed with the consideration of integration into existing Advanced Microcontroller Bus Architecture (AMBA) systems in future.
机译:自从1980年代初引入小波变换以来,小波变换已在多个领域做出了重大贡献,例如图像处理和压缩,雷达信号分析,数值分析,生物医学信号处理,医学成像和数字信号处理。小波分析的主要优势是与其他转换相比,额外的时间和频率信息。但是,离散小波变换(DWT)需要非常大的内存需求,并且计算量很大,尤其是对于二维变换而言。通常,它具有二次计算复杂度。在这个项目中,我们提出了一种专门用于二维DWT的完全专用处理器。与其他现有架构相比,该架构旨在实现吞吐量,可伸缩性和灵活性方面的改进。这种体系结构所需的计算资源和内部存储器大大减少。对于2048x1566视频处理,建议的体系结构可以实现138fps的理论吞吐量。 DWT系统通过支持多达8个并行DWT引擎而具有可扩展性,并且每个DWT引擎都可以独立工作。 DWT系统架构非常灵活,可以根据不同的应用需求通过增加或减少DWT引擎来扩展性能。此外,在设计此体系结构时考虑了将来集成到现有的高级微控制器总线体系结构(AMBA)系统中。

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  • 作者

    Ang Boon Hui;

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  • 年度 2012
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  • 原文格式 PDF
  • 正文语种 en
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