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Proceedings Paper

Near-lossless compression by relaxation-labeled 3D prediction
Author(s): Bruno Aiazzi; Luciano Alparone; Stefano Baronti; Franco Lotti
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Paper Abstract

In this work, near-lossless compression, i.e., yielding strictly bounded reconstruction error, is proposed for high- quality data compression. An interframe causal DPCM scheme is presented for interframe compression of remotely sensed optical data, both multispectral and hyperspectral, as well as of volumetric medical data. The proposed encoder relies on a classified linear-regression prediction, followed by context- based arithmetic coding of the outcome prediction errors. It provides outstanding performances, both for reversible and for irreversible, i.e., near-lossless, compression. Coding time are affordable thanks to fast convergence of training. Decoding is always performed in real time.

Paper Details

Date Published: 29 December 2000
PDF: 12 pages
Proc. SPIE 4310, Visual Communications and Image Processing 2001, (29 December 2000); doi: 10.1117/12.411848
Show Author Affiliations
Bruno Aiazzi, Research Institute on Electromagnetic Waves (Italy)
Luciano Alparone, Univ. of Florence (Italy)
Stefano Baronti, Research Institute on Electromagnetic Waves (Italy)
Franco Lotti, Research Institute on Electromagnetic Waves (Italy)


Published in SPIE Proceedings Vol. 4310:
Visual Communications and Image Processing 2001
Bernd Girod; Charles A. Bouman; Eckehard G. Steinbach, Editor(s)

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