000086799 001__ 86799
000086799 005__ 20190812204939.0
000086799 02470 $$2ISI$$a000178044600108
000086799 037__ $$aCONF
000086799 245__ $$aAdaptive Entropy-Constrained Matching Pursuit Quantization
000086799 269__ $$a2001
000086799 260__ $$bEUSIPCO$$c2001
000086799 336__ $$aConference Papers
000086799 490__ $$aProceedings of EUSIPCO 2002
000086799 520__ $$aThis paper proposes an adaptive entropy-constrained Matching Pursuit coefficient quantization scheme. The quantization scheme takes benefit of the inherent properties of Matching Pursuit streams where coefficients energy decreases along with the iteration number. The decay rate can moreover be upper-bounded with an exponential curve driven by the redundancy of the dictionary. An optimal entropy-constrained quantization scheme can thus be derived once the dictionary is known. We propose here to approximate this optimal quantization scheme by adaptive quantization of successive coefficients whose actual values are used to update the quantization scheme parameters. This new quantization scheme is shown to outperform classical exponential quantization in the case of both random dictionaries and practical image coding with Gabor dictionaries.
000086799 6531_ $$aLTS2
000086799 6531_ $$aLTS4
000086799 700__ $$0240428$$g120906$$aVandergheynst, P.
000086799 700__ $$aFrossard, P.$$g101475$$0241061
000086799 773__ $$j2$$tProceedings of the IEEE ICIP$$q423-426
000086799 8564_ $$zn/a$$uhttps://infoscience.epfl.ch/record/86799/files/Vandergheynst2001_70.pdf$$s333311
000086799 909C0 $$xU10380$$pLTS2$$0252392
000086799 909C0 $$0252393$$xU10851$$pLTS4
000086799 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.tind.io:86799
000086799 937__ $$aEPFL-CONF-86799
000086799 970__ $$aVandergheynst2001_70/LTS
000086799 973__ $$sPUBLISHED$$aEPFL
000086799 980__ $$aCONF