Paper Info
Title
An Improved Discrete Particle Swarm Optimizer For Fast Vector Quantization Codebook Design
Abstract
For tree-structured vector quantizers (TSVQ), the codebook quality highly depends on the splitting criterion and the approach by which a specific node is selected and then be partitioned into new ones. Among several proposed TSVQs, maximum descent (MD) algorithm can produce high quality codebooks and reduce the computation time simultaneously. In this paper, under the basic structure of MD algorithm, we propose an improved discrete particle swarm optimizer with less computation cost and faster convergence rate than the conventional one, and then, based on which, a novel binary partitioning scheme for MD algorithm is presented. Experimental data show that the newly proposed algorithm can further improve the codebook quality while the computation time is almost equivalent to that of the MD algorithm.
Year
DOI
Venue
2007
10.1109/ICME.2007.4284689
2007 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-5
Keywords
Field
DocType
tree structure,clustering algorithms,convergence rate,vector quantization,particle swarm optimization,cost function,design optimization,convergence
Convergence (routing),Particle swarm optimization,Mathematical optimization,Linde–Buzo–Gray algorithm,Computer science,Vector quantization,Rate of convergence,Cluster analysis,Computation,Codebook
Conference
Citations 
PageRank 
References 
2
0.37
3
Authors
2
Name
Order
Citations
PageRank
Yu-Xuan Wang165032.68
Qiao-Liang Xiang224913.28