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 |
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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). ASME.