PCA-BASED SEEDING FOR IMPROVED VECTOR QUANTIZATION

G. Knittel, R. Parys

2009

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.

References

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Paper Citation


in Harvard Style

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 - Volume 1: IMAGAPP, (VISIGRAPP 2009) ISBN 978-989-8111-68-5, pages 96-99. DOI: 10.5220/0001808100960099


in Bibtex Style

@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 - Volume 1: IMAGAPP, (VISIGRAPP 2009)},
year={2009},
pages={96-99},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001808100960099},
isbn={978-989-8111-68-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)
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