Significance-linked connected component analysis+ for wavelet image coding
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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI 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.
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