By Topic

ST-ACO: Image Compression Using a New Adaptive Self-Organizing Tree Approach

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)
Cheng-Fa Tsai ; Dept. of Manage. Inf. Syst., Nat. Pingtung Univ. of Sci. & Technol., Pingtung ; Chao-Cheng Yang

This investigation presents an adaptive dynamic path selection algorithm (DPTSVQ) based on a self-organizing tree (S-TREE) using the threshold validity, called ST-ACO. ST-ACO employs an ant colony optimization framework (ACO) to adapt the nodes' threshold value incrementally. Furthermore, a fixed number of paths might impede self-organization, and result in searching on trap nodes. Experimental results indicate that the proposed algorithm not only generates better-quality decoded images than the S-TREE DoublePath algorithm, but also produces fewer candidate nodes than the MultiPath algorithm. Thus, the ST-ACO contributes hierarchical clusters, reducing the binary tree search bias by dynamic path searching and the adaptive threshold value in each node.

Published in:

Innovative Computing Information and Control, 2008. ICICIC '08. 3rd International Conference on

Date of Conference:

18-20 June 2008