http://www.cnr.it/ontology/cnr/individuo/prodotto/ID186740
Fuzzy blending of relaxation-labeled predictors for high-performance lossless image compression (Contributo in atti di convegno)
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- Fuzzy blending of relaxation-labeled predictors for high-performance lossless image compression (Contributo in atti di convegno) (literal)
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- 2000-01-01T00:00:00+01:00 (literal)
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- 10.1117/12.382921 (literal)
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Bruno Aiazzi; Luciano Alparone; Stefano Baronti (2000)
Fuzzy blending of relaxation-labeled predictors for high-performance lossless image compression
in SPIE Electronic Imaging 2000: Applications of Artificial Neural Networks in Image Processing V, 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/3962/1/41_1?isAuthorized=no (literal)
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- Proceedings of SPIE Electronic Imaging 2000: Applications of Artificial Neural Networks in Image Processing V (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
- Fuzzy blending of relaxation-labeled predictors for high-performance lossless image compression (literal)
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- N. M. Nasrabadi; A. K. Katsaggelos (literal)
- Abstract
- This paper deals with application of fuzzy and neural techniques to 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 predictors are calculated. Here, a trade off between the above two strategies is proposed, which relies on a space-varying linear-regression prediction obtained through fuzzy techniques, and is followed by context based statistical modeling of prediction errors, to enhance entropy coding. A thorough comparison with the most advanced methods in the literature, as well as an investigation of performance trends to work parameters, highlight the advantages of the fuzzy approach. (literal)
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