References Image Compression by Edge Pattern Learning Using Multilayer P Erc E Ptro N S
1 AKIBA, s., GOTO, K., YAMAMOTO, s., and WAKABAYASHI, H.: ‘Recent progress of transoceanic optical amplifier transmission system’. ECOC ’92, Invited papers, September 1992, pp. 719-726 2 IEEE standard 802.6, DQDB subnetwork of a MAN, July 1991 3 KOA& K. T., and OISHANSKY, R.: ‘Non-regenerative photonic dual bus with optical amplifiers’, to be published in I E E E Photonics Technol. Lett. 4 GILES, c. R., and DESURVIRE, E.: ‘Propagation of signal and noise in concatenated erbium-doped fiber amplifiers’, J. Lightwave Techno[., 1991, LT-9, pp. 147-154 KANNO, N., and ITO, K.: ‘Fiber-optic subcarrier multiplexing video transport employing multilevel QAM, IEEE J. Sel. Areas Commun., 1990, SAC-8, pp. 1313-1319 6 ELREFAIE, A. F., WAGNER, . E., ATLAS, D. A., and DAUT, D. G.: ‘Chromatic dispersion limitations in coherent lightwave transmission systems’, J. Lightwaue Technof., 1988, LT-6, pp. 704-709 7 AUWUIN, o., PRIGENT, L. and HAMAIDE, J.-P.: ‘Limitations on transoceanic optical communications by dispersion, Ken nonlinearity and amplifier noise’. ECOC ’91, Invited papers, September 1991, pp, 67-74 8 AGRAWAL, G. P.: ‘Nonlinear fiber optics’ (Academic Press, San Diego, CA, 1989) 5
Improved neural structures for image compression
The problem considered is the effective compression of image data. Compared to the many methods which allow for high compression factors (mainly based on the DCT), the proposed neural approaches offer, after a suitable training, comparable performance both in terms of perceived image quality and measured SNR. Moreover, they are able to perform in the same step the two main required operations of transformation and selection of the most significant terms, with a correspondingly smaller computational effort.<<ETX>>
Image data compression using a neural network model
Data compression and generalization capabilities are important for neural network models as learning machines. From this point of view, the image data compression characteristics of a neural network model are examined. The applied network model is a feedforward-type, three-layered network with the backpropagation learning algorithm. The implementation of this model on a hypercube parallel computer and its computation performance are described. Image data compression, generalization, and quantization characteristics are examined experimentally. Effects of learning using the discrete cosine transformation coefficients as initial connection weights are shown experimentally.<<ETX>>