Golomb-Rice coding parameter learning using deep belief network for hyperspectral image compression

While Golomb-Rice codes are optimal for geometrically distributed source, the practically achievable coding efficiency depends on the accuracy of the coding parameter estimated from the input data. Most existing methods are based on the assumption of geometric distribution and thus would suffer from a loss in coding efficiency if the underlying distribution deviates from the geometric distribution, which is usually the case in practice. We proposed a data-driven parameter estimation method without assuming the underlying distribution. We formulated the problem of choosing the best coding parameter for the given input data as a pattern classification problem. To this end, we trained a deep belief network using the data segments to be coded, along with their “labels”, which are the optimal coding parameters that yield the shortest codewords. Simulations on data synthesized using statistical models, as well as data in hyperspectral image coding showed that the proposed deep learning method tended to be more robust than several state-of-the-art parameter estimation methods, with the capability to further improve the accuracies of these methods.