Reversible compression of 2D and 3D data through a fuzzy linear prediction with context-based arithmetic coding

Bruno Aiazzi; Pasquale S. Alba; Luciano Alparone; Stefano Baronti

doi:10.1117/12.330363

6 November 1998 Reversible compression of 2D and 3D data through a fuzzy linear prediction with context-based arithmetic coding

Bruno Aiazzi, Pasquale S. Alba, Luciano Alparone, Stefano Baronti

Author Affiliations +

Proceedings Volume 3456, Mathematics of Data/Image Coding, Compression, and Encryption; (1998) https://doi.org/10.1117/12.330363
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

A novel method for reversible compression of 2D and 3D data is presented. An adaptive spatial prediction is followed by a context-based classification with arithmetic coding of the outcome residuals. Prediction of a pixel to be encoded is obtained from the fuzzy-switching of a set of linear predictors. The coefficients of each predictor are calculated to minimize prediction MSE for pixels belonging to a cluster in the hyperspace of graylevel patterns lying on a preset causal neighborhood. In the 3D cases, piles both on the current slice and on previously encoded slices may be used. The size and shape of the causal neighborhood, as well as the number of predictors to be switched, may be chosen before running the algorithm and determine the trade-off between coding performances and computational cost. The method exhibits impressive performances, for both 2D and 3D data, mainly thanks to the optimality of predictors, due to their skill in fitting data patterns.

Citation Download Citation

Bruno Aiazzi, Pasquale S. Alba, Luciano Alparone, and Stefano Baronti "Reversible compression of 2D and 3D data through a fuzzy linear prediction with context-based arithmetic coding", Proc. SPIE 3456, Mathematics of Data/Image Coding, Compression, and Encryption, (6 November 1998); https://doi.org/10.1117/12.330363

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