Significance-linked connected component analysis+ for wavelet image coding

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI-COLUMBIA AT AUTHOR'S REQUEST.] 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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical and computer engineering (MU)

Grantor dc:publisher

University of Missouri--Columbia

Year dc:date.issued

2010

Author dc:creator

• Zhang, Cheng, 1985-

Advisors dc:contributor.advisor

• Zhuang, Xinhua
• He, Zhihai, 1973-

Statement dc:rights

• Access to files is limited to the University of Missouri--Columbia with SSO login.

Language dc:language.iso

eng, English

DOI

https://doi.org/10.32469/10355/10659

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/10659