A distributed implementation of fuzzy clustering and switching of linear regression models for lossless compression of imagery and 3D data

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.