http://www.cnr.it/ontology/cnr/individuo/prodotto/ID186741
Lossless image compression by adaptive contextual encoding (Contributo in atti di convegno)
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- Lossless image compression by adaptive contextual encoding (Contributo in atti di convegno) (literal)
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- 2000-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1117/12.383001 (literal)
- Alternative label
Bruno Aiazzi; Luciano Alparone; Stefano Baronti (2000)
Lossless image compression by adaptive contextual encoding
in SPIE Electronic Imaging 2000: Image and Video Communications and Processing 2000, San Jose, CA, USA, 25-28 Gennaio 2000
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- Bruno Aiazzi; Luciano Alparone; Stefano Baronti (literal)
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- http://spiedigitallibrary.org/proceedings/resource/2/psisdg/3974/1/654_1?isAuthorized=no (literal)
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- Proceedings of SPIE Electronic Imaging 2000: Image and Video Communications and Processing 2000 (literal)
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- \"Nello Carrara\" Research Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, I-50127 Firenze, Italy
Department of Electronics and Telecommunications, University of Florence, Via di Santa Marta, 3, I-50139 Firenze, Italy
\"Nello Carrara\" Research Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, I-50127 Firenze, Italy (literal)
- Titolo
- Lossless image compression by adaptive contextual encoding (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
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- B. Vasudev; T. Russell Hsing; A. G. Tescher; R. L. Stevenson (literal)
- Abstract
- This paper deals with the reversible intraframe compression of grayscale images. With reference to a spatial DPCM scheme, prediction 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 minimum MSE (MMSE) predictors are calculated. In this paper, a trade off between the above two strategies is proposed, which relies on a classified linear-regression prediction obtained through fuzzy techniques, and is followed by context based statistical modeling of the outcome prediction errors, to enhance entropy coding. A thorough performances comparison with the most advanced methods in the literature highlights the advantages of the fuzzy approach. (literal)
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