Binary Image Coding using Cellular Neural Networks
Research output: Contribution to conferences › Paper › Contributed › peer-review
Contributors
Abstract
Image coding still is an important research field in image processing. Although storage capacities increase permanently, image file sizes are of high interest in the area of image transmission. E.g. in the Internet the number of bytes transmitted is directly correlated to the costs and the time consumption for the transmission. Furthermore, because of the extremely high amount of data, in video processing efficient compression methods are always point of interest. In this contribution a new approach of image coding is presented, which uses the relatively new paradigm of Cellular Neural Networks (CNN). CNN are massively parallel computing arrays, which are perfectly suited for high speed image processing. Furthermore, their robustness is another outstanding feature of CNN hardware implementations, so that they predominate many other neural network implementations.
Details
| Original language | English |
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| Pages | 1149-1152 |
| Number of pages | 4 |
| Publication status | Published - 2003 |
| Peer-reviewed | Yes |
| Externally published | Yes |
Conference
| Title | International Joint Conference on Neural Networks 2003 |
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| Duration | 20 - 24 July 2003 |
| City | Portland, OR |
| Country | United States of America |
External IDs
| ORCID | /0000-0001-7436-0103/work/173513945 |
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