|dc.description.abstract||Due to the rapid growth in information handling
and transmission, there is a serious demand for more
efficient data compression schemes.
compression schemes address themselves to speech,
visual and alphanumeric coded data. This thesis is
concerned with the compression of visual data given in
the form of still or moving pictures. such data is highly
correlated spatially and in the context domain.
A detailed study of some existing data
compression systems is presented, in particular, the
performance of DPCM was analysed by computer simulation,
and the results examined both subjectively and
objectively. The adaptive form of the prediction encoder
is discussed and two new algorithms proposed, which
increase the definition of the compressed image and
reduce the overall mean square error.
Two novel systems are proposed for image
compression. The first is a bit plane image coding system
based on a hierarchic quadtree structure in a
transmission domain, using the Hadamard transform as a
kernel. Good compression has been achieved from this
scheme, particularly for images with low detail.
The second scheme uses a learning automata to
predict the probability distribution of the grey levels
of an image related to its spatial context and position.
An optimal reward/punishment function is proposed such
that the automata converges to its steady state within
4000 iterations • such a high speed of convergence
together with Huffman coding results in efficient
compression for images and is shown to be applicable to
other types of data. .
The performance and evaluation of all the
proposed .'systems have been tested by computer simulation
and the results presented both quantitatively and
qualitatively."The advantages and disadvantages of each
system are discussed and suggestions for improvement.