Significance-linked Connected Component Analysis Plus with new DOR and context model
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] In this thesis, a new efficient wavelet image coding algorithm Significance-Linked Connected Component Analysis Plus (SLCCA+) is introduced. SLCCA+ has a new data organization and representation (DOR) which is developed from the conventional SLCCA. The new DOR is using an improved map searching method, cluster-group with significant-linkage. And a modified context-based arithmetic coding method is used. SLCCA+ can achieve a better result than the conventional SLCCA. And the codec has fast speed since it does not need any optimal bit allocation procedure.
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