Simultaneous segmentation, compression, and denoising of signals
using polyharmonic local sine transform and
minimum description length (with E. Woei), in Proceedings of
13th IEEE Statistical Signal Processing Workshop, pp. 315-320, 2005.
Abstract
We propose a new approach to simultaneously segment, compress, and denoise
a given noisy signal by combining our compact signal representation scheme
called polyharmonic local sine transform (PHLST)
and the minimum description length (MDL) criterion.
PHLST first generates a redundant set of local pieces of an input signal
each of which is supported on a dyadic subinterval and is approximated
by a combination of an algebraic polynomial of low order (e.g., linear or cubic)
and a trigonometric polynomial. This combination of polynomials compensates
their shortcomings and yields a compact representation of the local piece.
To select the best nonredundant combination of the local pieces from
this redundant set, we use the MDL criterion with and without actually
quantizing the relevant parameters. The resulting representation
gives rise to simultaneous segmentation, compression, and denoising
of the original data.
We shall demonstrate its superiority over the best basis algorithm
using the local cosine dictionary with the sparsity criterion.
Get the full paper (corrected version as of 01/26/07): PDF file.
Get the official version (older than the above) via doi:10.1109/SSP.2005.1628613.
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