Date of Award
2016
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Electrical and Computer Engineering
Committee Chair
W. David Pan
Committee Member
Craig Newborn
Committee Member
Mark Tillman
Committee Member
B. Earl Wells
Committee Member
Dongsheng Wu
Subject(s)
Image processing--Digital techniques., Image compression., JPEG (Image coding standard)
Abstract
This work addresses efficient lossless compression of binary images. To this end, we designed several novel compression techniques, including one method known as the biased run-Length coding method. In this method, we first partition a binary image into equally sized blocks. We then convert the binary pixels within each block into a block symbol. In contrast to conventional approaches where all symbols are run-length coded, our method run-length codes only the most probable block symbols, followed by Huffman coding on the run-lengths. The other less probable block symbols will be coded with a separate Huffman code. Tests on NASA's AVIRIS dataset showed that this method could provide significant improvements over various binary image compression techniques (including those based on JBIG2 and lossless JPEG 2000 standards) on regions-of-interest (ROI) maps of hyperspectral images. Furthermore, we provided a detail analysis on the biased run-length coding method using statistical models. The analytical results agreed very well with the results on empirical data. Lastly, we introduced a modified model based on Markov random fields, which can generate a large number of binary images iteratively. Simulation results showed that the biased run-length coding method significantly outperformed the arithmetic code and JBIG2 method. This study also gave rise to a dual biased run-length method, which can provide further compression gains on images with a larger number of foreground objects.
Recommended Citation
Liaghati, Amir Leon, "Design and analysis of biased run-length coding methods and their applications in lossless compression of bi-level ROI maps in hyperspectral images" (2016). Dissertations. 100.
https://louis.uah.edu/uah-dissertations/100