By Topic

Bi-Level Image Compression Estimating the Markov Order of Dependencies

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Alcaraz-Corona, S. ; Electron. & Telecommun. Center, Inst. Tecnol. de Monterrey, Monterrey, Mexico ; Rodriguez-Dagnino, R.M.

This paper presents a bi-level image compression method based on chain codes and entropy coders. However, the proposed method also includes an order estimation process to estimate the order of dependencies that may exist among the chain code symbols prior to the entropy coding stage. For each bi-level image, the method first obtains its chain code representation and then estimates its order of symbol dependencies. This order value is used to find the conditional and joint symbol probabilities corresponding to our newly defined Markov model. Our order estimation process is based on the Bayesian information criterion (BIC), a statistically based model selection technique that has proved to be a consistent order estimator. In our experiments, we show how our order estimation process can help achieve more efficient compression levels by providing comparisons against some of the most commonly used image compression standards such as the Graphics Interchange Format (GIF), Joint Bi-level Image Experts Group (JBIG), and JBIG2.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:4 ,  Issue: 3 )