Title :
Binary image coding using cellular neural networks
Author :
Feiden, Dirk ; Tetzlaff, Ronald
Author_Institution :
Inst. of Appl. Phys., Frankfurt Univ., Germany
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.
Keywords :
binary codes; cellular neural nets; data compression; image coding; stability; video signal processing; Internet; binary image coding; cellular neural networks; compression methods; image file sizes; image processing; image transmission; parallel computing arrays; robustness; storage capacities; video processing; Cellular neural networks; Costs; Image coding; Image communication; Image processing; Image storage; Internet; Parallel processing; Robustness; Video compression;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223853