C.N. Manikopoulos, Neural network approach to DPCM system design for image coding, IEE Proc.-I 139 (5) (October 1992) 501}507.  Home/Search   Document Not in Database   Summary   Related Articles

....Non linear predictive coding, however, is very limited due to the di culties involved in optimizing the coe cients extraction to obtain the best possible predictive values. Under this circumstance, a neural network provides a very promising approach in optimizing non linear predictive coding [17,42]. With a linear AR model, predictive coding can be described by the following equation: #v p#v , 3.9) where p represents the predictive value for the pixel which is to be encoded in the next step. Its neighbouring pixels, X , are used by the linear model to produce the ....

....images such as edges, contours, etc. highorder terms are added to improve the predictive performance. This corresponds to a non linear AR model expressed as follows: #2#v . 3. 11) Hence, another so called functional link type neural network can be designed [42] to implement this type of a non linear AR model with high order terms. The structure of the network is illustrated in Fig. 10. It contains only two layers of neurones, one for input and the other for output. Coupling weights, #w # #, between the input layer and the output layer are trained ....

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C.N. Manikopoulos, Neural network approach to DPCM system design for image coding, IEE Proc.-I 139 (5) (October 1992) 501}507.

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