Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
Fundamentals of digital image processing
Fundamentals of digital image processing
Vector quantization and signal compression
Vector quantization and signal compression
Ten lectures on wavelets
Multi-frame compression: theory and design
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Algorithm 820: A flexible implementation of matching pursuit for Gabor functions on the interval
ACM Transactions on Mathematical Software (TOMS)
EURASIP Journal on Applied Signal Processing
Harmonic decomposition of audio signals with matching pursuit
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
A posteriori quantization of progressive matching pursuit streams
IEEE Transactions on Signal Processing
SNR scalability based on matching pursuits
IEEE Transactions on Multimedia
Quantized overcomplete expansions in IRN: analysis, synthesis, and algorithms
IEEE Transactions on Information Theory
Data compression and harmonic analysis
IEEE Transactions on Information Theory
On denoising and best signal representation
IEEE Transactions on Information Theory
Gaussian source coding with spherical codes
IEEE Transactions on Information Theory
Sparse representations in unions of bases
IEEE Transactions on Information Theory
A successive approximation vector quantizer for wavelet transform image coding
IEEE Transactions on Image Processing
In-loop atom modulus quantization for matching pursuit and its application to video coding
IEEE Transactions on Image Processing
Very low bit-rate video coding based on matching pursuits
IEEE Transactions on Circuits and Systems for Video Technology
Video compression using matching pursuits
IEEE Transactions on Circuits and Systems for Video Technology
Modulus quantization for matching-pursuit video coding
IEEE Transactions on Circuits and Systems for Video Technology
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Matching pursuit decompositions have been employed for signal coding. For this purpose, matching pursuit coefficients need to be quantized. However, their behavior has been shown to be chaotic in some cases; posing difficulties to their modeling and quantizer design. In this work, a different approach is presented. Instead of trying to model the statistics of matching pursuit coefficients, the statistics of the angle between the residue signal and the element selected in each iteration of the matching pursuit are studied, what allows to model matching pursuits coefficients indirectly. This approach results in a simple statistical model. This is so because one observes that the statistics of such angles do not vary substantially after the first matching pursuit iteration, and can be approximately modeled as independent and identically distributed. Moreover, it is also observed that the probability density functions of matching pursuit angles are reasonably modeled by a single probability density function. This function depends only on the dictionary employed and not on the signal source. The derived statistical model is validated by employing it to design Lloyd-Max quantizers for matching pursuit coefficients. The Lloyd-Max quantizers obtained show good ratexdistortion performance when compared to the state-of-the-art methods.