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Efficient image compression system with a CMOS transform imager.

机译:具有CMOS变换成像器的高效图像压缩系统。

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摘要

This research focuses on the implementation of the efficient image compression system among the many potential applications of a transform imager system. The study includes implementing the image compression system using a transform imager, developing a novel image compression algorithm for the system, and improving the performance of the image compression system through efficient encoding and decoding algorithms for vector quantization.;A transform imaging system is implemented using a transform imager, and the baseline JPEG compression algorithm is implemented and tested to verify the functionality and performance of the transform imager system. The computational reduction in digital processing is investigated from two perspectives, algorithmic and implementation. Algorithmically, a novel wavelet-based embedded image compression algorithm using dynamic index reordering vector quantization (DIRVQ) is proposed for the system. DIRVQ makes it possible for the proposed algorithm to achieve superior performance over the embedded zero-tree wavelet (EZW) algorithm and the successive approximation vector quantization (SAVQ) algorithm. However, because DIRVQ requires intensive computational complexity, additional focus is placed on the efficient implementation of DIRVQ, and highly efficient implementation is achieved without a compromise in performance.
机译:这项研究的重点是在变换成像器系统的许多潜在应用中实现高效的图像压缩系统。该研究包括使用变换成像仪实现图像压缩系统,为该系统开发新颖的图像压缩算法以及通过用于矢量量化的有效编码和解码算法来提高图像压缩系统的性能。变换成像仪,然后实施并测试基线JPEG压缩算法,以验证变换成像仪系统的功能和性能。从算法和实现两个角度研究了数字处理中的计算约简。在算法上,针对该系统提出了一种基于小波的嵌入式图像压缩算法,该算法利用动态索引重排序矢量量化(DIRVQ)。 DIRVQ使所提出的算法有可能获得优于嵌入式零树小波(EZW)算法和逐次逼近矢量量化(SAVQ)算法的性能。但是,由于DIRVQ需要大量的计算复杂性,因此将额外的重点放在DIRVQ的有效实现上,并且可以在不牺牲性能的情况下实现高效的实现。

著录项

  • 作者

    Lee, Jungwon.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 115 p.
  • 总页数 115
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:38:10

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