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Authors: G. Knittel and R. Parys

Affiliation: Tuebingen University, Germany

Keyword(s): Vector quantization, Image compression, Principal component analysis, Clustering.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Coding and Compression ; Image Enhancement and Restoration ; Image Formation and Preprocessing

Abstract: We propose a new method for finding initial codevectors for vector quantization. It is based on Principal Component Analysis and uses error-directed subdivision of the eigenspace in reduced dimensionality. Addi-tionally, however, we include shape-directed split decisions based on eigenvalue ratios to improve the visual appearance. The method achieves about the same image quality as the well-known k-means++ method, while providing some global control over compression priorities.

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Paper citation in several formats:
Knittel, G. and Parys, R. (2009). PCA-BASED SEEDING FOR IMPROVED VECTOR QUANTIZATION. In Proceedings of the First International Conference on Computer Imaging Theory and Applications (VISIGRAPP 2009) - IMAGAPP; ISBN 978-989-8111-68-5, SciTePress, pages 96-99. DOI: 10.5220/0001808100960099

@conference{imagapp09,
author={G. Knittel. and R. Parys.},
title={PCA-BASED SEEDING FOR IMPROVED VECTOR QUANTIZATION},
booktitle={Proceedings of the First International Conference on Computer Imaging Theory and Applications (VISIGRAPP 2009) - IMAGAPP},
year={2009},
pages={96-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001808100960099},
isbn={978-989-8111-68-5},
}

TY - CONF

JO - Proceedings of the First International Conference on Computer Imaging Theory and Applications (VISIGRAPP 2009) - IMAGAPP
TI - PCA-BASED SEEDING FOR IMPROVED VECTOR QUANTIZATION
SN - 978-989-8111-68-5
AU - Knittel, G.
AU - Parys, R.
PY - 2009
SP - 96
EP - 99
DO - 10.5220/0001808100960099
PB - SciTePress