Freisleben, Bernd ; Mengel, Maximilian (1993)
Image Compression with Self-Organizing Networks.
New Trends in Neural Computation. Proceedings.
Conference or Workshop Item, Bibliographie
Abstract
In this paper we evaluate and compare the performance of self-organizing neural networks applied to the task of image compression. The networks investigated are two-layered architectures with linear neurons, and variants of Hebbian learning rules are used to reduce the dimensionality of the inputs while preserving a maximum of information in the output units. Although in theory all networks considered are effectively equivalent to performing the Karhunen-Loeve transform, which is the optimal image compression method in the sense that it allows linear reconstruction of the input information with minimal squared error, the results obtained in practice reveal significant differences between the networks. An experimental study has been conducted to demonstrate these differences and thus some light is shed on the suitability of self-organizing neural networks for image compression, particularly in comparison to more conventional methods.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 1993 |
| Creators: | Freisleben, Bernd ; Mengel, Maximilian |
| Type of entry: | Bibliographie |
| Title: | Image Compression with Self-Organizing Networks |
| Language: | German |
| Date: | 1993 |
| Publisher: | Springer, Berlin, Heidelberg |
| Series: | Lecture Notes in Computer Science (LNCS); 686 |
| Event Title: | New Trends in Neural Computation. Proceedings |
| Abstract: | In this paper we evaluate and compare the performance of self-organizing neural networks applied to the task of image compression. The networks investigated are two-layered architectures with linear neurons, and variants of Hebbian learning rules are used to reduce the dimensionality of the inputs while preserving a maximum of information in the output units. Although in theory all networks considered are effectively equivalent to performing the Karhunen-Loeve transform, which is the optimal image compression method in the sense that it allows linear reconstruction of the input information with minimal squared error, the results obtained in practice reveal significant differences between the networks. An experimental study has been conducted to demonstrate these differences and thus some light is shed on the suitability of self-organizing neural networks for image compression, particularly in comparison to more conventional methods. |
| Uncontrolled Keywords: | Hebbian learning for image compression, Principal component analysis, Self-organizing networks |
| Divisions: | UNSPECIFIED 20 Department of Computer Science 20 Department of Computer Science > Interactive Graphics Systems |
| Date Deposited: | 16 Apr 2018 09:09 |
| Last Modified: | 16 Apr 2018 09:09 |
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