Computational complexity reduction in fractal image compression by adaptive codebook clustering

dc.contributor.advisor Dickerson, Julie
dc.contributor.author Zimmermann, Klaus
dc.date.accessioned 2024-09-18T18:41:25Z
dc.date.available 2024-09-18T18:41:25Z
dc.date.issued 1996
dc.description.abstract Fractal image coding has several advantages over conventional coding techniques such as faster and iterative decoding, higher compression, and resolution independence. These are highly desired coding properties in a broadband network environment. The weakness of a fractal coding system however is the computational complexity of the encoder, which prohibits a real-time implementation and makes fractal coding an unsuitable coding method in a symmetric point-to-point communication system. It is the objective of this thesis to develop and apply a new technique for image block classification and analysis in fractal image compression. This technique will be based on the concept of multi-dimensional keys that form a metric space. The Linde-Buzo-Gray algorithm is employed to cluster these keys so that one can search efficiently through the clusters for the nearest neighbor. A fractal coding scheme is presented that uses multi-dimensional keys to improve and accelerate block classification. As a consequence, the coding scheme can increase image fidelity while decreasing encoding time. The software implementation of this coding scheme validates the approach.
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/PrMB66oz
dc.language.iso en
dc.title Computational complexity reduction in fractal image compression by adaptive codebook clustering
dc.type thesis en_US
dc.type.genre thesis en_US
dspace.entity.type Publication
relation.isDegreeOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.department Department of Electrical and Computer Engineering
thesis.degree.discipline Electrical Engineering
thesis.degree.level Masters
thesis.degree.name Master of Science
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