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The recently presented recursive least squares dictionary learning algorithm (RLS-DLA) is tested in a general image compression application. Dictionaries are learned in the pixel domain and in the 9/7 wavelet domain, and then tested in a straightforward compression scheme. Results are compared with state-of-the-art compression methods. The proposed compression scheme using RLS-DLA learned dictionaries...
We consider the problem of lossy compression of images using sparse representations from overcomplete dictionaries. This problem is in principle easy to solve using standard algorithms for convex programming, but often the large dimensions render such an approach intractable. We present a highly efficient method based on recently developed first-order methods, which enables us to compute sparse approximations...
Signal approximation using a linear combination of basis from an overcomplete dictionary has been proven to be an NP-Hard problem. By selecting a smaller number of basis than the span of the signal, we achieve lossy compression in exchange for a small reconstruction error. Several algorithms have been researched that reduce the complexity of the selection problem, sacrificing the optimality of the...
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