Open Science Research Excellence
%0 Journal Article
%A S. Anna Durai and  E. Anna Saro
%D 2008 
%J  International Journal of Computer, Electrical, Automation, Control and Information Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 16, 2008
%T Image Mapping with Cumulative Distribution Function for Quick Convergence of Counter Propagation Neural Networks in Image Compression
%V 16
%X In general the images used for compression are of
different types like dark image, high intensity image etc. When these
images are compressed using Counter Propagation Neural Network,
it takes longer time to converge. The reason for this is that the given
image may contain a number of distinct gray levels with narrow
difference with their neighborhood pixels. If the gray levels of the
pixels in an image and their neighbors are mapped in such a way that
the difference in the gray levels of the neighbor with the pixel is
minimum, then compression ratio as well as the convergence of the
network can be improved. To achieve this, a Cumulative Distribution
Function is estimated for the image and it is used to map the image
pixels. When the mapped image pixels are used the Counter
Propagation Neural Network yield high compression ratio as well as
it converges quickly.
%P 1084 - 1089