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 coefficient 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 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.