Abstract:
In this thesis dynamic clustering and neural network techniques have been used
for image compression. First we cluster the whole image dynamically up to an
expected Peak Signal Noise Ratio (PSNR) and Mean Square Error (MSE).
Thus number of clusters depends on the similarity threshold, target PSNR and
MSE. Each cluster has its own cluster prototype and cluster identification
number. Naturally, each pixel of the image is represented by its cluster
identification number. According to the locality property of an image many
consecutive pixels happen to be the members of the same cluster. So, a
structure consisting of cluster number and repeat count to represent a number
of consecutive pixels have been proposed for better compression ratio. Then
the cluster number part is further compressed using a neural network. Here the
co-ordinates of the pixels are inputs and the corresponding cluster numbers are
the outputs of the neural network. Cumulative cluster number is used instead of
ordinary cluster number to make the neural network small for larger
compression ratio.
