Lc. Wang et al., A MODULAR NEURAL-NETWORK VECTOR PREDICTOR FOR PREDICTIVE IMAGE-CODING, IEEE transactions on image processing, 7(8), 1998, pp. 1198-1217
Citations number
19
Categorie Soggetti
Computer Science Software Graphycs Programming","Computer Science Theory & Methods","Engineering, Eletrical & Electronic","Computer Science Software Graphycs Programming","Computer Science Theory & Methods
In this paper, we present a modular neural network vector predictor th
at improves the predictive component of a predictive vector quantizati
on (PVQ) scheme. The proposed vector prediction technique consists of
five dedicated predictors (experts), where each expert predictor is op
timized for a particular class of input vectors. An input vector is cl
assified into one of five classes, based on its directional variances.
One expert predictor is optimized for stationary blocks, and each of
the other four expert predictors are optimized to predict horizontal,
vertical, 45 degrees, and 135 degrees diagonally oriented edge-blocks,
respectively. An integrating unit is then used to select or combine t
he outputs of the experts in order to form the final output Of the mod
ular network, Therefore, no side information is transmitted to the rec
eiver about the selected predictor or the integration of the predictor
s. Experimental results show that the proposed scheme gives an improve
ment of 1.7 dB over a single multilayer perceptron on (MLP) predictor,
Furthermore, if the information about the predictor selection is sent
to the receiver, the improvement could bit up to 3 dB over a single M
LP predictor. The perceptual quality of the predicted images is also s
ignificantly improved.