Adaptive Entropy-Constrained Matching Pursuit Quantization

This 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.


Publié dans:
Proceedings of the IEEE ICIP, 2, 423-426
Année
2001
Publisher:
EUSIPCO
Mots-clefs:
Laboratoires:




 Notice créée le 2006-06-14, modifiée le 2019-08-12

n/a:
Télécharger le document
PDF

Évaluer ce document:

Rate this document:
1
2
3
 
(Pas encore évalué)