A l(1)-NORM PRESERVING MOTION-COMPENSATED TRANSFORM FOR SPARSE APPROXIMATION OF IMAGE SEQUENCES
2010 (English)Conference paper (Refereed)
This paper discusses an adaptive non-linear transform for image sequences that aims to generate a l(1)-norm preserving sparse approximation for efficient coding. Most sparse approximation problems employ a linear model where images are represented by a basis and a sparse set of coefficients. In this work, however, we consider image sequences where linear measurements are of limited use due to motion. We present a motion-adaptive non-linear transform for a group of pictures that outputs common and detail coefficients and that minimizes the l(1) norm of the detail coefficients while preserving the overall l(1) norm. We demonstrate that we can achieve a smaller l(1) norm of the detail coefficients when compared to that of motion-adaptive linear measurements. Further, the decay of normalized absolute coefficients is faster than that of motion-adaptive linear measurements.
Place, publisher, year, edition, pages
IEEE , 2010. 902-905 p.
, International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Sparse approximation, l(1) norm, motion compensation, image sequence processing
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-32254ISI: 000287096000212ScopusID: 2-s2.0-78049391332ISBN: 978-1-4244-4296-6OAI: oai:DiVA.org:kth-32254DiVA: diva2:411451
IEEE International Conference on Acoustics, Speech, and Signal Processing
QC 201104182011-04-182011-04-112016-05-02Bibliographically approved