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- W2025479859 abstract "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 further derive an MSR-based image coding approach to encode the quantized sparse coefficients with reduced reconstructed errors. Experimental results demonstrate rapid convergence of the proposed dictionary-learning algorithm and improved rate-distortion performance over other competitive image compression schemes both subjectively and quantitatively, validating the effectiveness of the proposed approach." @default.
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- W2025479859 date "2014-11-01" @default.
- W2025479859 modified "2023-10-13" @default.
- W2025479859 title "Dictionary Learning for Image Coding Based on Multisample Sparse Representation" @default.
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- W2025479859 doi "https://doi.org/10.1109/tcsvt.2014.2319652" @default.
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