TY - EJOUR
DO - 10.1016/j.sigpro.2005.05.023
AB - 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 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 choses the number of functions from the redundant dictionary and the wavelet basis.
T1 - Image compression using an edge adapted redundant dictionary and wavelets
IS - 3
DA - 2006
AU - Peotta, L.
AU - Granai, L.
AU - Vandergheynst, P.
JF - Signal Processing
SP - 444-456
VL - 86
EP - 444-456
ID - 87336
KW - Coding
KW - Greedy approximation
KW - Image compression
KW - Image representation
KW - LTS2
KW - Matching Pursuit
KW - Redundant dictionaries
KW - Sparse representation
KW - Wavelet
UR - http://infoscience.epfl.ch/record/87336/files/Peotta2004_1152.pdf
ER -