TY - CPAPER
AB - Very 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 images in a redundant geometrically inspired library of functions, followed by suitable coding techniques. Best N-term 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. Finally the last step in our algorithm is an enhancement layer that encodes the residual image: in our simulation we have used a genuine embedded wavelet codec.
T1 - Very low bit rate image coding using redundant dictionaries
DA - 2003
AU - Peotta, L.
AU - Granai, L.
AU - Vandergheynst, P.
JF - Proceedings of the SPIE, Wavelets: Applications in Signal and Image Processing X
SP - 228-239
VL - 5207
EP - 228-239
PB - SPIE
ID - 86966
KW - approximation
KW - Coding
KW - dictionaries
KW - Greedy
KW - Image
KW - LTS2
KW - Matching
KW - Pursuit
KW - Redundant
KW - representation
KW - Sparse
KW - Wavelet
UR - http://infoscience.epfl.ch/record/86966/files/Peotta2003_45.pdf
ER -