Image Compression Method Using Improved PSO Vector Quantization

VQ coding is a powerful technique in digital image compression. Conversional methods such as classic LBG algorithm always generate local optimal codebook. In this paper, we introduce Particle Swarm Optimization (PSO) cluster method to build high quality codebook for image compression. We also set th...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 490 - 495
Main Authors Chen, Qian, Yang, Jiangang, Gou, Jin
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Pattern recognition. Digital image processing. Computational geometry
Vector quantization
Optimal algorithm
Evolutionary algorithm
Data compression
Image compression
Decoding
Digital image
Neural network
Vector method
Image reconstruction
Convergence speed
Image quality
Codebook
Swarm intelligence
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Summary:VQ coding is a powerful technique in digital image compression. Conversional methods such as classic LBG algorithm always generate local optimal codebook. In this paper, we introduce Particle Swarm Optimization (PSO) cluster method to build high quality codebook for image compression. We also set the result of LBG algorithm to initialize global best particle by which it can speed the convergence of PSO. Both image encoding and decoding process are simulated in our experiments. Results show that the algorithm is reliable and the reconstructed images get higher quality to images reconstructed by other methods.
ISBN:9783540283201
354028320X
3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539902_60