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

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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

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