Significance-linked connected component analysis+ for wavelet image coding

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Title from PDF of title page (University of Missouri--Columbia, viewed on April 29, 2011).Thesis advisors: Dr. Xinhua Zhuang and Dr. Zhihai He.M. S. University of Missouri-Columbia 2010.[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] The Significance-Linked Connected Component Analysis+ (SLCCA+) is a efficient wavelet image coding algorithm that extends SLCCA by using new data organization and representation (DOR) and SLCCA+ use a definition of context model for the adaptive arithmetic coding. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA+ outperforms SLCCA. For example, for the Lena image, at 0.1 bit/pixel, SLCCA+ outperforms SLCCA by 0.1 dB in PSNR. This outstanding performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.Includes bibliographical references (pages 82-87)