Image compression via K-means and SLIC superpixel approaches

DOI

10.2991/icmmita-16.2016.185How to use a DOI?

Keywords

Image Compression; K-means; Superpixel

Abstract

Image compression is a key component in the transmission and storage process of image data. The major objective of image compression is to reduce the irrelevance and redundancy of the image data in order to store and transmit them in an efficient form. One of the practical approaches is to reduce the original large color space to a considerable small scale. In detail, we implement this image compression method via two approaches: K-means, which directly clustering the colors to nearest centroids, and the SLIC superpixel approaches, which relies on generating computationally efficient and perceptually meaningful image segments. Experiment results reveal that both of our approaches could effectively compress the image size.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaolin Luo
PY  - 2017/01
DA  - 2017/01
TI  - Image compression via K-means and SLIC superpixel approaches
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications
PB  - Atlantis Press
SP  - 1008
EP  - 1012
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmita-16.2016.185
DO  - 10.2991/icmmita-16.2016.185
ID  - Luo2017/01
ER  -