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In this paper, we present a novel image codec by leveraging sparse representation strategy for geometric pattern encoding. Specifically, we propose a Multiple Learned Geometric Dictionaries (MLGD) solution to explore various texture patterns of images, and use different dictionaries to encode homogenous smooth components and heterogeneous directional components. Profiting from model proficiency, our...
In this brief we propose a multisample sparse representation (MSR)-based online dictionary-learning approach to encode images more efficiently. To minimize the reconstructed error while handling a variety of image samples, we develop a multisample sparse representation method capable of obtaining sparser coefficients combined with learning dictionaries on-the-fly. With a well-learned dictionary, we...
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