Image compression with a dynamic autoassociative neural network

Andres Rios; Mansur R. Kabuka

Image compression with a dynamic autoassociative neural network

Andres Rios, Mansur R. Kabuka

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

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 language	English
Title of host publication	Intelligent Engineering Systems Through Artificial Neural Networks
Editors	C.H. Dagli, L.I. Burke, Y.C. Shin
Place of Publication	Fairfield, NJ, United States
Publisher	ASME
Pages	503-510
Number of pages	8
Volume	2
State	Published - Dec 1 1992
Event	Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA Duration: Nov 15 1992 → Nov 18 1992

Other

Other	Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
City	St.Louis, MO, USA
Period	11/15/92 → 11/18/92

ASJC Scopus subject areas

Software

Fingerprint Dive into the research topics of 'Image compression with a dynamic autoassociative neural network'. Together they form a unique fingerprint.

Cite this

@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 = dec,

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",

note = "null ; Conference date: 15-11-1992 Through 18-11-1992",

}

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

AN - SCOPUS:0027105465

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

Y2 - 15 November 1992 through 18 November 1992

ER -