A new algorithm is introduced which is related to matching pursuit but allows updating more than one coding coefficient per iteration: the updated coefficients correspond to mutually orthogonal elements of the dictionary. Coding experiments on natural images show that the new method achieves the same trade-off as matching pursuit between number of coding coefficients and reconstruction error, b...

We study the problem of recovering the sparsity pattern of block-sparse signals from noise-corrupted measurements. A simple, efficient recovery method, namely, a block-version of the orthogonal matching pursuit (OMP) method, is considered in this paper and its behavior for recovering the block-sparsity pattern is analyzed. We provide sufficient conditions under which the block-version of the OM...

Today’s video codecs employ motion compensated prediction in combination with block matching techniques. These techniques, although achieving some level of adaptivity in their latest versions [1], continue to rely on the decomposition of frames on a set of artificial primitives: blocks. This paper presents a new approach to video coding. A geometrically adaptive image decomposition scheme using...

We propose a codebook adaptation algorithm for matching pursuit low bit rate video compression. Matching pursuit dictionary is a set of basis functions. In practice, a function is approximated as a vector. According to the incoming data, the basis functions are modified similar to the minimum-point-finding algorithm in stochastic regression. Although matching pursuit low bit rate compressions h...

The problem of optimal approximation of members of a vector space by a linear combination of members of a large overcomplete library of vectors is of importance in many areas including image and video coding, image analysis, control theory, and statistics. Finding the optimal solution in the general case is mathematically intractable. Matching pursuit, and its orthogonal version, provide greedy...

Sound recordings include transients and sustained parts. Their analysis with a basis expansion is not rich enough to represent eeciently all such components. Pursuit algorithms choose the decomposition vectors depending upon the signal properties. The dictionary among which these vectors are selected is much larger than a basis. Matching Pursuit is fast to compute, but can provide coarse repres...

Computing the optimal expansion of a signal in a redundant dictionary of waveforms is an NP-hard problem. We introduce a greedy algorithm, called a matching pursuit, which computes a sub-optimal expansion. The dictionary waveforms which best match a signal's structures are chosen iteratively. An orthogonalized version of the matching pursuit is also developed. Matching pursuits are general proc...

A technique for designing frames to use with vector selection algorithms, for example Matching Pursuits (MP), is presented. The design algorithm is iterative and requires a training set of signal vectors. An MP algorithm chooses frame vectors to approximate each training vector. Each vector in the frame is then adjusted by using the residuals for the training vectors which used that particular ...