| Lossless compression of color sequences using optimal linear prediction theory. | |
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MedLine Citation:
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PMID: 18854256 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In this paper, we present a novel technique that uses the optimal linear prediction theory to exploit all the existing redundancies in a color video sequence for lossless compression purposes. The main idea is to introduce the spatial, the spectral, and the temporal correlations in the autocorrelation matrix estimate. In this way, we calculate the cross correlations between adjacent frames and adjacent color components to improve the prediction, i.e., reduce the prediction error energy. The residual image is then coded using a context-based Golomb-Rice coder, where the error modeling is provided by a quantized version of the local prediction error variance. Experimental results show that the proposed algorithm achieves good compression ratios and it is roboust against the scene change problem. |
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Authors:
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Stefano Andriani; Giancarlo Calvagno |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society Volume: 17 ISSN: 1057-7149 ISO Abbreviation: IEEE Trans Image Process Publication Date: 2008 Nov |
Date Detail:
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Created Date: 2008-10-15 Completed Date: 2008-12-09 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9886191 Medline TA: IEEE Trans Image Process Country: United States |
Other Details:
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Languages: eng Pagination: 2102-11 Citation Subset: IM |
Affiliation:
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Department of Information Engineering, University of Padova, 35131 Padova, Italy. stefano.andriani@ieee.org |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Color* Colorimetry / methods* Data Compression / methods* Image Enhancement / methods* Image Interpretation, Computer-Assisted / methods* Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted* Video Recording / methods* |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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