INTELLIGENT SYSTEM FOR IMAGE COMPRESSION

Adnan Khashman, Kamil Dimililer

Abstract

The parallel processing capability of neural networks provides efficient means for processing images with large amount of data. Image compression using Discrete Cosine Transforms (DCT) is a lossy compression method where at higher compression ratios the quality of the compressed images is reduced, thus the need for finding an optimum compression ratio that combines high compression and good quality. This paper suggests that the image intensity can affect the choice of an optimum compression ratio. A neural network will be trained to establish the non-linear relationship between the image intensity and its compression ratios in search for an optimum ratio. Experimental results suggest that a trained neural network can relate image intensity or pixel values to its compression ratio and thus can be successfully used to predict optimum DCT compression ratios for different images.

References

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Paper Citation


in Harvard Style

Khashman A. and Dimililer K. (2007). INTELLIGENT SYSTEM FOR IMAGE COMPRESSION . In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-89-4, pages 451-454. DOI: 10.5220/0002402204510454


in Bibtex Style

@conference{iceis07,
author={Adnan Khashman and Kamil Dimililer},
title={INTELLIGENT SYSTEM FOR IMAGE COMPRESSION},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2007},
pages={451-454},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002402204510454},
isbn={978-972-8865-89-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - INTELLIGENT SYSTEM FOR IMAGE COMPRESSION
SN - 978-972-8865-89-4
AU - Khashman A.
AU - Dimililer K.
PY - 2007
SP - 451
EP - 454
DO - 10.5220/0002402204510454