Fast implementation of two-level compression method using QM-coder

We deal with bi-level image compression. Modern methods consider the bi-level image as a high order Markovian source, and by exploiting this characteristic, can attain better performance. At a first glance, the increasing of the order of the Markovian model in the modelling process should yield a hi...

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Published in:DCC (Los Alamitos, Calif.) p. 459
Main Authors: Nguyen-Phi, K., Weinrichter, H.
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 1997
Subjects:
Arithmetic
Context modeling
Hydrogen
Image coding
Testing
ISBN:9780818677618, 0818677619
ISSN:1068-0314
Online Access:Get full text
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Abstract We deal with bi-level image compression. Modern methods consider the bi-level image as a high order Markovian source, and by exploiting this characteristic, can attain better performance. At a first glance, the increasing of the order of the Markovian model in the modelling process should yield a higher compression ratio, but in fact, it is not true. A higher order model needs a longer time to learn (adaptively) the statistical characteristic of the source. If the source sequence, or the bi-level image in this case, is not long enough, then we do not have a stable model. One simple way to solve this problem is the two-level method. We consider the implementation aspects of this method. Instead of using the general arithmetic coder, an obvious alternative is using the QM-coder, thus reducing the memory used and increasing the execution speed. We discuss some possible heuristics to increase the performance. Experimental results obtained with the ITU-T test images are given.
AbstractList The implementation aspects of two-level compression method using QM coder is discussed. The use of QM coder reduces the memory used increases the execution speed. Some possible heuristics to increasing the performance are discussed.
We deal with bi-level image compression. Modern methods consider the bi-level image as a high order Markovian source, and by exploiting this characteristic, can attain better performance. At a first glance, the increasing of the order of the Markovian model in the modelling process should yield a higher compression ratio, but in fact, it is not true. A higher order model needs a longer time to learn (adaptively) the statistical characteristic of the source. If the source sequence, or the bi-level image in this case, is not long enough, then we do not have a stable model. One simple way to solve this problem is the two-level method. We consider the implementation aspects of this method. Instead of using the general arithmetic coder, an obvious alternative is using the QM-coder, thus reducing the memory used and increasing the execution speed. We discuss some possible heuristics to increase the performance. Experimental results obtained with the ITU-T test images are given.
Author Nguyen-Phi, K.
Weinrichter, H.
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Snippet We deal with bi-level image compression. Modern methods consider the bi-level image as a high order Markovian source, and by exploiting this characteristic,...
The implementation aspects of two-level compression method using QM coder is discussed. The use of QM coder reduces the memory used increases the execution...
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StartPage 459
SubjectTerms Arithmetic
Context modeling
Hydrogen
Image coding
Testing
Title Fast implementation of two-level compression method using QM-coder
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