Paper Info
Title
Modeling of contours in wavelet domain for generalized lifting image compression
Abstract
This paper introduces the design of context-based models of contours in the wavelet domain, which are used to construct generalized lifting (GL) mappings for image compression. The GL context-based mapping may significantly reduce the signal energy and the resulting bitrate. Here, we propose a strategy to define a reduced set of structured models to design the GL. The models capture the contour structures and are contrast-invariant. Initial experimental results applying the strategy on a wavelet subband exhibit potential gains. Iterations of the GL scheme as well as an adaptive entropy coding strategy may increase the coding gain.
Year
DOI
Venue
2009
10.1109/ICASSP.2009.4959761
ICASSP
Keywords
Field
DocType
wavelet subband exhibit potential,adaptive entropy,coding gain,image compression,contour structure,generalized lifting image compression,wavelet domain,gl context-based mapping,generalized lifting,context-based model,gl scheme,encoding,entropy coding,decorrelation,signal processing,decoding,signal energy,context modeling,wavelet transforms,probability density function,wavelets
Coding gain,Entropy encoding,Pattern recognition,Computer science,Generalized lifting,Context model,Energy (signal processing),Artificial intelligence,Image compression,Wavelet transform,Wavelet
Conference
ISSN
Citations 
PageRank 
1520-6149
2
0.45
References 
Authors
9
3
Name
Order
Citations
PageRank
Julio C. Rolón1122.48
Antonio Ortega24720493.26
Philippe Salembier360387.65