Document Detail


Lossless compression of color sequences using optimal linear prediction theory.
MedLine Citation:
PMID:  18854256     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Stefano Andriani; Giancarlo Calvagno
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Publication Detail:
Type:  Journal Article    
Journal Detail:
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:
Created Date:  2008-10-15     Completed Date:  2008-12-09     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  United States    
Other Details:
Languages:  eng     Pagination:  2102-11     Citation Subset:  IM    
Affiliation:
Department of Information Engineering, University of Padova, 35131 Padova, Italy. stefano.andriani@ieee.org
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MeSH Terms
Descriptor/Qualifier:
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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