Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Efficient image representation by anisotropic refinement in matching pursuit
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Hybrid video coding based on bidimensional matching pursuit
EURASIP Journal on Applied Signal Processing
Harmonic decomposition of audio signals with matching pursuit
IEEE Transactions on Signal Processing
Analysis of low bit rate image transform coding
IEEE Transactions on Signal Processing
A posteriori quantization of progressive matching pursuit streams
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
On the exponential convergence of matching pursuits in quasi-incoherent dictionaries
IEEE Transactions on Information Theory
Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images
IEEE Transactions on Image Processing
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
Low-rate and flexible image coding with redundant representations
IEEE Transactions on Image Processing
Wavelet-domain approximation and compression of piecewise smooth images
IEEE Transactions on Image Processing
Very low bit-rate video coding based on matching pursuits
IEEE Transactions on Circuits and Systems for Video Technology
Sparse approximation of images inspired from the functional architecture of the primary visual areas
EURASIP Journal on Applied Signal Processing
Compression of facial images using the K-SVD algorithm
Journal of Visual Communication and Image Representation
A Bayesian Lasso via reversible-jump MCMC
Signal Processing
Dictionary learning for image prediction
Journal of Visual Communication and Image Representation
Hi-index | 0.02 |
Low bit rate image coding is an important problem regarding applications such as storage on low memory devices or streaming data on the internet. The state of the art in image compression is to use two-dimensional (2-D) wavelets. The advantages of wavelet bases lie in their multiscale nature and in their ability to sparsely represent functions that are piecewise smooth. Their main problem on the other hand, is that in 2-D wavelets are not able to deal with the natural geometry of images, i.e. they cannot sparsely represent objects that are smooth away from regular submanifolds. In this paper we propose an approach based on building a sparse representation of the edge part of images in a redundant geometrically inspired library of functions, followed by suitable coding techniques. Best N-terms non-linear approximations in general dictionaries is, in most cases, a NP-hard problem and sub-optimal approaches have to be followed. In this work we use a greedy strategy, also known as Matching Pursuit to compute the expansion. The residual, that we suppose to be the smooth and texture part, is then coded using wavelets. A rate distortion optimization procedure chooses the number of functions from the redundant dictionary and the wavelet basis.