Image compression with a dynamic autoassociative neural network

Andres Rios, Mansur R. Kabuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Image compression using neural networks has been attempted with some promise. Among the architectures, feedforward backpropagation networks (FFBPN) have been used in several attempts. We propose an architecture and an improved training method to attempt to solve the shortcomings of traditional data compression based on feedforward networks - the dynamic autoassociation neural network (DANN). The results of several tasks presented to DANN based compression are compared with the performance of FFBPN based system. These results indicate that DANN is superior to FFBPN for data compression.

Original languageEnglish
Title of host publicationIntelligent Engineering Systems Through Artificial Neural Networks
EditorsC.H. Dagli, L.I. Burke, Y.C. Shin
Place of PublicationFairfield, NJ, United States
PublisherASME
Pages503-510
Number of pages8
Volume2
StatePublished - Dec 1 1992
EventProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA
Duration: Nov 15 1992Nov 18 1992

Other

OtherProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
CitySt.Louis, MO, USA
Period11/15/9211/18/92

Fingerprint

Image compression
Backpropagation
Neural networks
Data compression

ASJC Scopus subject areas

  • Software

Cite this

Rios, A., & Kabuka, M. R. (1992). Image compression with a dynamic autoassociative neural network. In C. H. Dagli, L. I. Burke, & Y. C. Shin (Eds.), Intelligent Engineering Systems Through Artificial Neural Networks (Vol. 2, pp. 503-510). Fairfield, NJ, United States: ASME.

Image compression with a dynamic autoassociative neural network. / Rios, Andres; Kabuka, Mansur R.

Intelligent Engineering Systems Through Artificial Neural Networks. ed. / C.H. Dagli; L.I. Burke; Y.C. Shin. Vol. 2 Fairfield, NJ, United States : ASME, 1992. p. 503-510.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rios, A & Kabuka, MR 1992, Image compression with a dynamic autoassociative neural network. in CH Dagli, LI Burke & YC Shin (eds), Intelligent Engineering Systems Through Artificial Neural Networks. vol. 2, ASME, Fairfield, NJ, United States, pp. 503-510, Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92, St.Louis, MO, USA, 11/15/92.
Rios A, Kabuka MR. Image compression with a dynamic autoassociative neural network. In Dagli CH, Burke LI, Shin YC, editors, Intelligent Engineering Systems Through Artificial Neural Networks. Vol. 2. Fairfield, NJ, United States: ASME. 1992. p. 503-510
Rios, Andres ; Kabuka, Mansur R. / Image compression with a dynamic autoassociative neural network. Intelligent Engineering Systems Through Artificial Neural Networks. editor / C.H. Dagli ; L.I. Burke ; Y.C. Shin. Vol. 2 Fairfield, NJ, United States : ASME, 1992. pp. 503-510
@inproceedings{942ec369454d4754a48f46cb879eb64f,
title = "Image compression with a dynamic autoassociative neural network",
abstract = "Image compression using neural networks has been attempted with some promise. Among the architectures, feedforward backpropagation networks (FFBPN) have been used in several attempts. We propose an architecture and an improved training method to attempt to solve the shortcomings of traditional data compression based on feedforward networks - the dynamic autoassociation neural network (DANN). The results of several tasks presented to DANN based compression are compared with the performance of FFBPN based system. These results indicate that DANN is superior to FFBPN for data compression.",
author = "Andres Rios and Kabuka, {Mansur R.}",
year = "1992",
month = "12",
day = "1",
language = "English",
volume = "2",
pages = "503--510",
editor = "C.H. Dagli and L.I. Burke and Y.C. Shin",
booktitle = "Intelligent Engineering Systems Through Artificial Neural Networks",
publisher = "ASME",

}

TY - GEN

T1 - Image compression with a dynamic autoassociative neural network

AU - Rios, Andres

AU - Kabuka, Mansur R.

PY - 1992/12/1

Y1 - 1992/12/1

N2 - Image compression using neural networks has been attempted with some promise. Among the architectures, feedforward backpropagation networks (FFBPN) have been used in several attempts. We propose an architecture and an improved training method to attempt to solve the shortcomings of traditional data compression based on feedforward networks - the dynamic autoassociation neural network (DANN). The results of several tasks presented to DANN based compression are compared with the performance of FFBPN based system. These results indicate that DANN is superior to FFBPN for data compression.

AB - Image compression using neural networks has been attempted with some promise. Among the architectures, feedforward backpropagation networks (FFBPN) have been used in several attempts. We propose an architecture and an improved training method to attempt to solve the shortcomings of traditional data compression based on feedforward networks - the dynamic autoassociation neural network (DANN). The results of several tasks presented to DANN based compression are compared with the performance of FFBPN based system. These results indicate that DANN is superior to FFBPN for data compression.

UR - http://www.scopus.com/inward/record.url?scp=0027105465&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027105465&partnerID=8YFLogxK

M3 - Conference contribution

VL - 2

SP - 503

EP - 510

BT - Intelligent Engineering Systems Through Artificial Neural Networks

A2 - Dagli, C.H.

A2 - Burke, L.I.

A2 - Shin, Y.C.

PB - ASME

CY - Fairfield, NJ, United States

ER -