Algorithms and methods for sparse approximation in structured dictionaries
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Compression efficiency is mainly driven by redundancy of the overcomplete set of functions chosen for the signal decomposition. In Matching Pursuit algorithms for example, the redundancy of the dictionary influences the convergence of the residual energy. ...
Matching pursuit decomposes a signal into a linear expansion of functions selected from a redundant dictionary, isolating the signal structures that are coherent with respect to a given dictionary. In this paper we focus on the Matching Pursuit representat ...
Wavelet or sub–band coding has been quite successful in compression applications, and this success can be attributed in part to the good approximation properties of wavelets. In this paper, we revisit rate–distortion (RD) bounds for the wavelet approximati ...
The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictio- nary of time–frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal adaptive. In ...
Institute of Electrical and Electronics Engineers1999
The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictionary of time-frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal-adaptive. In th ...
This paper studies quantization error in the context of Matching Pursuit coded streams and proposes a new coefficient quantization scheme taking benefit of the Matching Pursuit properties. The coefficients energy in Matching Pursuit indeed decreases with t ...
This paper presents a new image representation method based on anisotropic refinement. It has been shown that wavelets are not optimal to code 2-D objects which need true 2-D dictionaries for efficient approximation. We propose to use rotations and anisotr ...
