By Topic

Segmentation of Source Symbols for Adaptive Arithmetic Coding

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

3 Author(s)
Liang Zhang ; Commun. Res. Centre Canada, Ottawa, ON, Canada ; Demin Wang ; Dong Zheng

Adaptive arithmetic coding is a general technique for coding source symbols of a stochastic process based on an adaptive model. The adaptive model provides measures of the statistics of source symbols and is updated, along with encoding/decoding processes, when more encoded/decoded symbols are fed as samples to the adaptive model. The coding performance depends on how well the adaptive model fits the statistics of source symbols. If the number of source symbols is large and the number of samples is small, the adaptive model may not be able to provide valid measures of the statistics, which results in an inefficient coding performance of the adaptive arithmetic coder. To this end, this paper presents segmentation of source symbols to improve the performance of the adaptive arithmetic coder. Each source symbol is divided into several segments. Each segment is separately coded with an adaptive arithmetic coder. With this division, possible values of each segment are concentrated within a small range. Given the limited number of samples, this concentration leads to a better fit of the adaptive model to the statistics of source symbols and therefore to an improvement of the coding efficiency. The proposed coding algorithm is applied to lossless motion vector coding for video transmission as an application example to show its performance improvement and coding gains.

Published in:

Broadcasting, IEEE Transactions on  (Volume:58 ,  Issue: 2 )