Close Approximation of Kullback–Leibler Divergence for Sparse Source Retrieval

Emna Ghodhbani, Mounir Kaaniche, Amel Benazza-Benyahia

Abstract:

In this paper, we propose a fast and accurate ap-
proximation of the Kullback-Leibler divergence (KLD) between
two Bernoulli-Generalized Gaussian (Ber-GG) distributions. Such
distribution has been found to be well-suited for modeling sparse
signals like wavelet-based representations. Based on high bitrate
approximations of the entropy of quantized Ber-GG sources, we
provide a close approximation of the KLD without resorting
to the conventional time-consuming Monte Carlo estimation ap-
proach. The developed approximation formula is then validated
in the context of depth map and stereo image retrieval.