Close Approximation of Kullback–Leibler Divergence for Sparse Source Retrieval

In this letter, we propose a fast and accurate approximation of the Kullback-Leibler divergence (KLD) between two Bernoulli-Generalized Gaussian (Ber-GG) distributions. Such a distribution has been found to be well suited for modeling sparse signals like wavelet-based representations. On the basis of 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 approach. The developed approximation formula is then validated in the context of depth map and stereo image retrieval.