Magnitude Sensitive Image Compression

Enrique Pelayo, David Buldain, Carlos Orrite

2013

Abstract

This paper introduces the Magnitude Sensitive Competitive Learning (MSCL) algorithm as a reliable and effi- cient approach for selective image compression. MSCL is a neural network that has the property of distributing the unit centroids in certain data-distribution zones according to a target magnitude locally calculated for every unit. This feature can be used for image compression to define the block images that will be compressed by Vector Quantization in a later step. As a result, areas of interest receive a lower compression than other parts in the image. Following this approach higher quality in the salient areas of a compressed image is achieved in relation to other methods.

Paper Citation

in Harvard Style

Pelayo E., Buldain D. and Orrite C. (2013). Magnitude Sensitive Image Compression . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 370-380. DOI: 10.5220/0004552103700380

in Bibtex Style

@conference{ncta13,
author={Enrique Pelayo and David Buldain and Carlos Orrite},
title={Magnitude Sensitive Image Compression},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={370-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004552103700380},
isbn={978-989-8565-77-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Magnitude Sensitive Image Compression
SN - 978-989-8565-77-8
AU - Pelayo E.
AU - Buldain D.
AU - Orrite C.
PY - 2013
SP - 370
EP - 380
DO - 10.5220/0004552103700380