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. given.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:354882 |
| Date | January 1985 |
| Creators | Abdul-Amir, Said |
| Publisher | De Montfort University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2086/10681 |
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