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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Feiden, D. Tetzlaff, R. |
| Copyright Year | 2003 |
| Description | Author affiliation: Inst. of Appl. Phys., Frankfurt Univ., Germany (Feiden, D.; Tetzlaff, R.) |
| Abstract | Image coding still is an important research field in image processing. Although storage capacitates 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 (CN). 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. |
| Sponsorship | Int. Neural Network Soc. IEEE Neural Networks Soc |
| Starting Page | 1149 |
| Ending Page | 1152 |
| File Size | 254792 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780378989 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2003.1223853 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2003-07-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image coding Cellular neural networks Image processing Image storage Image communication Internet Costs Video compression Parallel processing Robustness |
| Content Type | Text |
| Resource Type | Article |
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