Contact Information:
Xinhua Zhuang
Department of Computer Engineering and Computer Science
University of Missouri-Columbia
Columbia, MO 65211
Jozsef Vass
Department of Computer Engineering and Computer Science
University of Missouri-Columbia
Columbia, MO 65211
In recent years, we have seen an impressive advance in wavelet image
coding. The success is mainly attributed to innovative strategies for
data organization and representation of wavelet-transformed images which
exploit the statistical properties in a wavelet pyramid one way or the
other. In the Multimedia Communications and Visualization Lab, a very
high performance image coding algorithm termed significance-linked
connected component analysis (SLCCA) was developed by
Bing-Bing Chai, Jozsef Vass, and Xinhua Zhuang. Extensive computer experiment
demonstrate that SLCCA is among the best wavelet coding algorithms.
There are two types of subband decomposition which are commonly used in
image compression, i.e., uniform and pyramidal decomposition. Uniform
decomposition divides an image into equal-sized
subbands. By contrast, pyramidal decomposition represents an octave-band
(dyadic) decomposition, offering a multiresolution representation of
the image. Most of the subband image coders published recently are based on
pyramidal (or dyadic) wavelet decomposition.
Data organization plays key role in the recent success of wavelet image
coding algorithms. There have been three such high performance wavelet
image coder developed, Shapiro's embedded zerotree wavelet coder (EZW) [1],
Servetto et al. morphological representation of wavelet data
(MRWD) [2], and Said and Pearlman's set partitioning in
hierarchical trees (SPIHT) [3]. Both EZW and SPIHT exploit
cross-subband dependency of insignificant wavelet coefficients
while MRWD does within-subband clustering of significant
wavelet coefficients.
Different from EZW and SPIHT, where insignificant wavelet coefficients
are represented by a highly constrained tree or set partitioned tree structure,
SLCCA follows the spirit of MRWD by directly clustering the significance
field. Furthermore, SLCCA strengthens MRWD by exploiting the cross-scale
dependency as well.
The coding results show that SLCCA outperforms
several top performance wavelet coder such as EZW,
SPIHT, and MRWD.
The coding performance is demonstrated on the following images (in GIF format):
Images for true performance comparison (in PGM format):
[1] J. Shapiro, "Embedded image coding using
zerotrees of wavelet coefficients," IEEE Transactions on Signal
Processing, vol. 41, no. 12, pp. 3445-3462, Dec. 1993.
[2] S. Servetto, K. Ramchandran, and M. Orchard, "Wavelet based image
coding via morphological prediction of significance," Proceedings of
IEEE ICIP-95, Oct. 1995, pp. 530-533.
[3] A. Said and W.A. Pearlman, "A new, fast, and efficient image codec
based on set partitioning in hierarchical trees," IEEE Transactions
on Circuits and Systems for Video Technology, vol. 6, no. 3, pp. 243-250,
June 1996.
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CECS Multimedia Communications and Visualization Laboratory
