HIERARCHICAL LEARNING OF SPARSE IMAGE REPRESENTATIONS USING STEERED MIXTURE-OF-EXPERTS

Previous research showed highly efficient compression results for low bit-rates using Steered Mixture-of-Experts (SMoE), higher rates still pose a challenge due to the non-convex optimization problem that becomes more difficult when increasing the number of components. Therefore, a novel estimation method based on Hidden Markov Random Fields is introduced taking spatial dependencies of neighboring pixels into account combined with a tree-structured splitting strategy. Experimental evaluations for images show that our approach outperforms state-of-the-art techniques using only one robust parameter set. For video and light field modeling even more gain can be expected.