In this work, a distributed implementation of a new method for reversible compression of both 2D and 3D data is presented. A classified prediction is first trained through fuzzy clustering; then, data decorrelation is accomplished by prediction in a fuzzy fashion. Context-based adaptive arithmetic coding is tailored to prediction errors to enhance entropy coding. Results and comparisons with other schemes are presented and discussed together with computational issues.
A distributed implementation of fuzzy clustering and switching of linear regression models for lossless compression of imagery and 3D data
B Aiazzi;L Alparone;S Baronti;F Lotti;
1998
Abstract
In this work, a distributed implementation of a new method for reversible compression of both 2D and 3D data is presented. A classified prediction is first trained through fuzzy clustering; then, data decorrelation is accomplished by prediction in a fuzzy fashion. Context-based adaptive arithmetic coding is tailored to prediction errors to enhance entropy coding. Results and comparisons with other schemes are presented and discussed together with computational issues.File in questo prodotto:
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