Lossless image coding deals with the problem of representing an image with a minimum number of binary bits from which the original image can be fully recovered without any loss of information. Most lossless image coding algorithms reach the goal of efficient compression by taking care of the spatial correlations and statistical redundancy lying in images. Context based algorithms are the typical algorithms in lossless image coding. One key probelm in context based lossless bi-level image coding algorithms is the design of context templates. By using carefully designed context templates, we can effectively employ the information provided by surrounding pixels in an image. In almost all image processing applications, image data is accessed in a raster scanning manner and is treated as 1-D integer sequence rather than 2-D data. In this thesis, we present a quadrisection scanning method which is better than raster scanning in that more adjacent surrounding pixels are incorporated into context templates. Based on quadrisection scanning, we develop several context templates and propose several image coding schemes for both sequential and progressive lossless bi-level image compression. Our results show that our algorithms perform better than those raster scanning based algorithms, such as JBIG1 used in this thesis as a reference. Also, the application of 1-D grammar based codes in lossless image coding is discussed. 1-D grammar based codes outperform those LZ77/LZ78 based compression utility software for general data compression. It is also effective in lossless image coding. Several coding schemes for bi-level image compression via 1-D grammar codes are provided in this thesis, especially the parallel switching algorithm which combines the power of 1-D grammar based codes and context based algorithms. Most of our results are comparable to or better than those afforded by JBIG1.

Lossless image coding deals with the problem of representing an image with a minimum number of binary bits from which the original image can be fully recovered without any loss of information. Most lossless image coding algorithms reach the goal of efficient compression by taking care of the spatial correlations and statistical redundancy lying in images. Context based algorithms are the typical algorithms in lossless image coding. One key probelm in context based lossless bi-level image coding algorithms is the design of context templates. By using carefully designed context templates, we can effectively employ the information provided by surrounding pixels in an image. In almost all image processing applications, image data is accessed in a raster scanning manner and is treated as 1-D integer sequence rather than 2-D data. In this thesis, we present a quadrisection scanning method which is better than raster scanning in that more adjacent surrounding pixels are incorporated into context templates. Based on quadrisection scanning, we develop several context templates and propose several image coding schemes for both sequential and progressive lossless bi-level image compression. Our results show that our algorithms perform better than those raster scanning based algorithms, such as JBIG1 used in this thesis as a reference. Also, the application of 1-D grammar based codes in lossless image coding is discussed. 1-D grammar based codes outperform those LZ77/LZ78 based compression utility software for general data compression. It is also effective in lossless image coding. Several coding schemes for bi-level image compression via 1-D grammar codes are provided in this thesis, especially the parallel switching algorithm which combines the power of 1-D grammar based codes and context based algorithms. Most of our results are comparable to or better than those afforded by JBIG1.

A synthesis methodology for multiple scan trees that considers output pin limitation, scan chain routing length, test application time and test data compression rate simultaneously is proposed in this thesis. Multiple scan trees, also known as a scan forest, greatly reduce test data volume and test application time in SOC testing. However, previous research on scan tree synthesis rarely considered issues such as routing length and output port limitation, and hence created scan trees with a large number of scan output ports and excessively long routing paths. The proposed algorithm provides a mechanism that effectively reduces test time and test data volume, and routing length under output port constraint. As a result, no output compressors are required, which significantly reduce the hardware overhead.

Thesis (M.S.E.C.E.)--University of Delaware, 2009. / Principal faculty advisor: Javier Garcia-Frias, Dept. of Electrical & Computer Engineering. Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 59-60). Also available in electronic version. Access restricted to campus users.

Thesis (M. Phil.)--University of Hong Kong, 1981.

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published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy