NAG CL Interface
c09ccc (dim1_multi_fwd)
1
Purpose
c09ccc computes the one-dimensional multi-level discrete wavelet transform (DWT). The initialization function
c09aac must be called first to set up the DWT options.
2
Specification
void |
c09ccc (Integer n,
const double x[],
Integer lenc,
double c[],
Integer nwl,
Integer dwtlev[],
Integer icomm[],
NagError *fail) |
|
The function may be called by the names: c09ccc, nag_wav_dim1_multi_fwd or nag_mldwt.
3
Description
c09ccc computes the multi-level DWT of one-dimensional data. For a given wavelet and end extension method,
c09ccc will compute a multi-level transform of a data array,
, for
,
using a specified number,
, of levels. The number of levels specified,
, must be no more than the value
returned in
nwlmax by the initialization function
c09aac for the given problem. The transform is returned as a set of coefficients for the different levels (packed into a single array) and a representation of the multi-level structure.
The notation used here assigns level to the input dataset, , with level being the first set of coefficients computed, with the detail coefficients, , being stored while the approximation coefficients, , are used as the input to a repeat of the wavelet transform. This process is continued until, at level , both the detail coefficients, , and the approximation coefficients, are retained. The output array, , stores these sets of coefficients in reverse order, starting with followed by .
4
References
None.
5
Arguments
-
1:
– Integer
Input
-
On entry: the number of elements, , in the data array .
Constraint:
this must be the same as the value
n passed to the initialization function
c09aac.
-
2:
– const double
Input
-
On entry:
x contains the one-dimensional input dataset
, for
.
-
3:
– Integer
Input
-
On entry: the dimension of the array
c.
c must be large enough to contain the number,
, of wavelet coefficients. The maximum value of
is returned in
nwc by the call to the initialization function
c09aac and corresponds to the DWT being continued for the maximum number of levels possible for the given data set. When the number of levels,
, is chosen to be less than the maximum, then
is correspondingly smaller and
lenc can be reduced by noting that the number of coefficients at each level is given by
for
in
c09aac and
for
,
or
, where
is the number of input data at that level and
is the filter length provided by the call to
c09aac. At the final level the storage is doubled to contain the set of approximation coefficients.
Constraint:
, where
is the number of approximation and detail coefficients that correspond to a transform with
nwlmax levels.
-
4:
– double
Output
-
On exit: let
denote the number of coefficients (of each type) produced by the wavelet transform at level
, for
. These values are returned in
dwtlev. Setting
and
, for
, the coefficients are stored as follows:
- , for
- Contains the level approximation coefficients, .
- , for
- Contains the level detail coefficients .
- , for
- Contains the level
detail coefficients, for .
-
5:
– Integer
Input
-
On entry: the number of levels, , in the multi-level resolution to be performed.
Constraint:
, where
is the value returned in
nwlmax (the maximum number of levels) by the call to the initialization function
c09aac.
-
6:
– Integer
Output
-
On exit: the number of transform coefficients at each level.
and contain the number, , of approximation and detail coefficients respectively, for the final level of resolution (these are equal); contains the number of detail coefficients, , for the ()th level, for .
-
7:
– Integer
Communication Array
-
On entry: contains details of the discrete wavelet transform and the problem dimension as setup in the call to the initialization function
c09aac.
On exit: contains additional information on the computed transform.
-
8:
– NagError *
Input/Output
-
The NAG error argument (see
Section 7 in the Introduction to the NAG Library CL Interface).
6
Error Indicators and Warnings
- NE_ALLOC_FAIL
-
Dynamic memory allocation failed.
See
Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
- NE_ARRAY_DIM_LEN
-
On entry,
lenc is set too small:
.
Constraint:
.
- NE_BAD_PARAM
-
On entry, argument had an illegal value.
- NE_INITIALIZATION
-
Either the initialization function has not been called first or array
icomm has been corrupted.
Either the initialization function was called with
or array
icomm has been corrupted.
On entry,
n is inconsistent with the value passed to the initialization function:
,
n should be
.
On entry,
nwl is larger than the maximum number of levels returned by the initialization function:
, maximum
.
- NE_INT
-
On entry, .
Constraint: .
- NE_INTERNAL_ERROR
-
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact
NAG for assistance.
See
Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
- NE_NO_LICENCE
-
Your licence key may have expired or may not have been installed correctly.
See
Section 8 in the Introduction to the NAG Library CL Interface for further information.
7
Accuracy
The accuracy of the wavelet transform depends only on the floating-point operations used in the convolution and downsampling and should thus be close to machine precision.
8
Parallelism and Performance
c09ccc is not threaded in any implementation.
The wavelet coefficients at each level can be extracted from the output array
c using the information contained in
dwtlev on exit (see the descriptions of
c and
dwtlev in
Section 5). For example, given an input data set,
, denoising can be carried out by applying a thresholding operation to the detail coefficients at every level. The elements
, for
, as described in
Section 5, contain the detail coefficients,
, for
and
, where
and
is the transformed noise term. If some threshold parameter
is chosen, a simple hard thresholding rule can be applied as
taking
to be an approximation to the required detail coefficient without noise,
. The resulting coefficients can then be used as input to
c09cdc in order to reconstruct the denoised signal.
See the references given in the introduction to this chapter for a more complete account of wavelet denoising and other applications.
10
Example
This example performs a multi-level resolution of a dataset using the Daubechies wavelet (see
in
c09aac) using zero end extensions, the number of levels of resolution, the number of coefficients in each level and the coefficients themselves are reused. The original dataset is then reconstructed using
c09cdc.
10.1
Program Text
10.2
Program Data
10.3
Program Results