Significance-linked Connected Component Analysis Plus with new DOR and context model
Metadata[+] Show full item record
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] 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.
Access is limited to the campuses of the University of Missouri.