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The advancement of fields such as multimedia and medical imaging, and the emergence of high resolution digital cameras have necessitated the acquisition, storage and transmission of high resolution digital images. Storage and transmission of such images is expensive in terms of bytes and bandwidth. Although the bandwidth of communication networks has been increasing continuously, the introduction of new services and the expansion of the existing ones demands an even higher bandwidth. There is a need for compressing these images to curtail the storage and transmission load. The research described in the thesis introduces two new hybrid image compress~on techniques for the efficient representation of stilI images, for lossy compression. The first is hybrid classified vector quantisation (HCVQ), which combines Mean-Removed Classified Vector Quantiser (MRCVQ) and Singular Value Decomposition (SVD), and the second technique is an improvement to the first technique and termed improved hybrid classified vectorquantisation (IHCVQ). . The novelty of these techniques lies in the process of codebook generation using SVD method based VQ, as well as using only one threshold instead of multiple threshold values as is the case with conventional classified VQ scheme. The efficiency of the proposed IHCVQ was examined for Magnetic Resonance Images (MR!). The proposed techniques were benchmarked with the ordinary vector quantiser which was generated using the k-means algorithm, the existing methods using CVQ scheme, and JPEG-2000. Simulation results indicated that the proposed approaches alleviate edge degradation and can reconstruct good visual quality images with high Peak Signal-to Noise-Ratio than the benchmarked techniques or competitive to them. Several visual experimental designs were carried out to evaluate the compressed reconstructed image quality by the proposed methods subjectively. The representative subjective method chosen to use in this research work is mean opinion score CMOS). NIOS is result of perception based subjective evaluation, which is 5-IeveI grading scale developed for subjective evaluation. Mean opinion score was' determined from the subjective visual assessment scale experiment for image quality of stilI-images.

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No description available.