http://www.cnr.it/ontology/cnr/individuo/prodotto/ID186729
Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding (Contributo in atti di convegno)
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- Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding (Contributo in atti di convegno) (literal)
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- 1999-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1117/12.372744 (literal)
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Bruno Aiazzi; Luciano Alparone; Stefano Baronti (1999)
Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding
in SPIE Annual Meeting 1999 (44th SPIE Annual Meeting): Mathematics of Data/Image Coding, Compression, and Encryption II, Denver, CO, USA, 19-23 Luglio 1999
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- Bruno Aiazzi; Luciano Alparone; Stefano Baronti (literal)
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- http://spiedigitallibrary.org/proceedings/resource/2/psisdg/3814/1/86_1?isAuthorized=no (literal)
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- Proceedings of the 44th SPIE Annual Meeting: Mathematics of Data/Image Coding, Compression, and Encryption II (literal)
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- \"Nello Carrara\" Research Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, I-50127 Firenze, Italy
Department of Electronic Engineering, University of Florence, Via S. Marta, 3, I-50139 Firenze, Italy
\"Nello Carrara\" Research Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, I-50127 Firenze, Italy (literal)
- Titolo
- Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding (literal)
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- Abstract
- This paper discusses the most recent trends in the reversible intraframe compression of grayscale images. With reference to a spatial DPCM scheme, prediction, either linar or nonlinear, may be accomplished in a space-varying fashion following two main strategies: adaptive, i.e., with predictors recalculated at each pixel position, and classified, in which image blocks, or pixels are preliminarily labeled into a number of statistical classes, for which optimum MMSE predictors are calculated. A trade-off between the above two strategies is proposed. It relies on a classified linear-regression prediction obtained through fuzzy techniques, followed by context-based modeling of the outcome prediction errors, to enhance entropy coding. The present scheme is a reworking of a fuzzy encoder previously presented by the authors. Now, predictors, instead of pixel intensity patterns, are fuzzy-clustered to find out optimized MMSE prediction classes, and a novel membership function measuring the fitness of prediction is adopted. A thorough performances comparison with the most advanced methods in the literature highlights advantages, and drawbacks as well, of the fuzzy approach. (literal)
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