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Digital signal processing implementation for near real-time wavelet transformation system for binary images

机译：二值图像近实时小波变换系统的数字信号处理实现

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摘要

Communication has become an essential function in our civilization. With the increasing demand for communication channels, it is now necessary to find ways to optimize the use of their bandwidth. One way to achieve this is by transforming the information before it is transmitted. This transformation can be performed by several techniques. One of the newest of these techniques is the use of wavelets. Wavelet transformation refers to the act of breaking down a signal into components called details and trends by using small waveforms that have a zero average in the time domain. After this transformation the data can be compressed by discarding the details, transmitting the trends. In the receiving end, the trends are used to reconstruct the image. In this work, the wavelet used for the transformation of an image will be selected from a library of available bases. The accuracy of the reconstruction, after the details are discarded, is dependent on the wavelets chosen from the wavelet basis library. The system developed in this thesis takes a 2-D image and decomposes it using a wavelet bank. A digital signal processor is used to achieve near real-time performance in this transformation task. A contribution of this thesis project is the development of DSP-based test bed for the future development of new real-time wavelet transformation algorithms.
机译：交流已成为我们文明的基本功能。随着对通信信道的需求的增长，现在有必要找到优化其带宽使用的方法。实现此目的的一种方法是在信息传输之前对其进行转换。该变换可以通过几种技术来执行。这些技术中的最新技术之一是小波的使用。小波变换是指通过使用在时域中均值为零的小波形将信号分解为称为细节和趋势的分量的行为。进行此转换后，可以通过丢弃细节并传输趋势来压缩数据。在接收端，趋势用于重建图像。在这项工作中，将从可用库中选择用于图像变换的小波。在丢弃细节之后，重建的精度取决于从小波基础库中选择的小波。本文开发的系统拍摄二维图像，并使用小波库分解。在此转换任务中，使用数字信号处理器来实现近乎实时的性能。本论文项目的一项贡献是开发了基于DSP的测试平台，用于未来新的实时小波变换算法的开发。

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    Alfonso Ovidio;

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  • 年度 2000
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