A vector predictor is an integral part of a predictive vector quantiza
tion (PVQ) coding scheme. However, the performance of a classical line
ar vector predictor is limited by its ability to exploit only the line
ar correlation between the blocks. Furthermore, its performance deteri
orates as the vector dimension (block size) is increased, especially w
hen predicting blocks that contain edge information. However, a nonlin
ear predictor exploits the higher-order correlations among the neighbo
ring blocks, and can predict edge blocks with increased accuracy. Beca
use the conventional techniques for designing a nonlinear predictor ar
e extremely complex and suboptimal due to the absence of a suitable mo
del for the source data, it is necessary to investigate new procedures
in order to design nonlinear vector predictors. In this paper, we hav
e investigated several neural network architectures that can be used t
o implement a nonlinear vector predictor, including the Multilayer Per
ceptron, the Functional Link network, and the Radial Basis Function ne
twork. We also evaluated and compared the performance of these neural
network predictors with that of a linear vector predictor. Our experim
ental results show that a neural network predictor can predict the blo
cks containing edges with a higher accuracy than a linear predictor. H
owever, the performance of a neural network predictor is comparable to
that of a linear predictor for predicting the stationary and shade bl
ocks.