c09ecc computes the two-dimensional multi-level discrete wavelet transform (DWT). The initialization function c09abc must be called first to set up the DWT options.

2 Specification

#include <nag.h>

void	c09ecc (Integer m, Integer n, const double a[], Integer lda, Integer lenc, double c[], Integer nwl, Integer dwtlvm[], Integer dwtlvn[], Integer icomm[], NagError *fail)

The function may be called by the names: c09ecc, nag_wav_dim2_multi_fwd or nag_mldwt_2d.

3 Description

c09ecc computes the multi-level DWT of two-dimensional data. For a given wavelet and end extension method, c09ecc will compute a multi-level transform of a matrix

A

, using a specified number,

n_{fwd}

, of levels. The number of levels specified,

n_{fwd}

, must be no more than the value

l_{\max}

returned in nwlmax by the initialization function c09abc 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

0

to the input matrix,

A

. Level 1 consists of the first set of coefficients computed: the vertical (

v_{1}

), horizontal (

h_{1}

) and diagonal (

d_{1}

) coefficients are stored at this level while the approximation (

a_{1}

) coefficients are used as the input to a repeat of the wavelet transform at the next level. This process is continued until, at level

n_{fwd}

, all four types of coefficients are stored. The output array,

C

, stores these sets of coefficients in reverse order, starting with

a_{n_{fwd}}

followed by

v_{n_{fwd}}, h_{n_{fwd}}, d_{n_{fwd}}, v_{n_{fwd} - 1}, h_{n_{fwd} - 1}, d_{n_{fwd} - 1}, \dots, v_{1}, h_{1}, d_{1}

4 References

None.

5 Arguments

1: $m$ – Integer Input

On entry: number of rows,

m

, of data matrix

A

Constraint: this must be the same as the value m passed to the initialization function c09abc.

2: $n$ – Integer Input

On entry: number of columns,

n

, of data matrix

A

Constraint: this must be the same as the value n passed to the initialization function c09abc.

3: $a [lda \times n]$ – const double Input

Note: the

(i, j)

th element of the matrix

A

is stored in

a [(j - 1) \times lda + i - 1]

On entry: the

m \times n

data matrix

A

4: $lda$ – Integer Input

On entry: the stride separating matrix row elements in the array a.

Constraint:

lda \geq m

5: $lenc$ – Integer Input

On entry: the dimension of the array c. c must be large enough to contain,

n_{ct}

, wavelet coefficients. The maximum value of

n_{ct}

is returned in nwct by the call to the initialization function c09abc and corresponds to the DWT being continued for the maximum number of levels possible for the given data set. When the number of levels,

n_{fwd}

, is chosen to be less than the maximum,

l_{\max}

, then

n_{ct}

is correspondingly smaller and lenc can be reduced by noting that the vertical, horizontal and diagonal coefficients are stored at every level and that in addition the approximation coefficients are stored for the final level only. The number of coefficients stored at each level is given by

3 \times ⌈ \bar{m} / 2 ⌉ \times ⌈ \bar{n} / 2 ⌉

for

mode = Nag_Periodic

in c09abc and

3 \times ⌊ (\bar{m} + n_{f} - 1) / 2 ⌋ \times ⌊ (\bar{n} + n_{f} - 1) / 2 ⌋

for

mode = Nag_HalfPointSymmetric

Nag_WholePointSymmetric

Nag_ZeroPadded

, where the input data is of dimension

\bar{m} \times \bar{n}

at that level and

n_{f}

is the filter length nf provided by the call to c09abc. At the final level the storage is

4 / 3

times this value to contain the set of approximation coefficients.

Constraint:

lenc \geq n_{ct}

, where

n_{ct}

is the total number of coefficients that correspond to a transform with nwl levels.

6: $c [lenc]$ – double Output

On exit: the coefficients of the discrete wavelet transform. If you need to access or modify the approximation coefficients or any specific set of detail coefficients then the use of c09eyc or c09ezc is recommended. For completeness the following description provides details of precisely how the coefficient are stored in c but this information should only be required in rare cases.

Let

q (i)

denote the number of coefficients (of each type) at level

i

, for

i = 1, 2, \dots, n_{fwd}

, such that

q (i) = dwtlvm [n_{fwd} - i] \times dwtlvn [n_{fwd} - i]

. Then, letting

k_{1} = q (n_{fwd})

and

k_{j + 1} = k_{j} + q (n_{fwd} - ⌈ j / 3 ⌉ + 1)

, for

j = 1, 2, \dots, 3 n_{fwd}

, the coefficients are stored in c as follows:

$c [i - 1]$ , for $i = 1, 2, \dots, k_{1}$

Contains the level

n_{fwd}

approximation coefficients,

a_{n_{fwd}}

$c [i - 1]$ , for $i = k_{j} + 1, \dots, k_{j + 1}$

Contains the level

n_{fwd} - ⌈ j / 3 ⌉ + 1

vertical, horizontal and diagonal coefficients. These are:

vertical coefficients if $j mod 3 = 1$ ;
horizontal coefficients if $j mod 3 = 2$ ;
diagonal coefficients if $j mod 3 = 0$ ,

for

j = 1, \dots, 3 n_{fwd}

7: $nwl$ – Integer Input

On entry: the number of levels,

n_{fwd}

, in the multi-level resolution to be performed.

Constraint:

1 \leq nwl \leq l_{\max}

, where

l_{\max}

is the value returned in nwlmax (the maximum number of levels) by the call to the initialization function c09abc.

8: $dwtlvm [nwl]$ – Integer Output

On exit: the number of coefficients in the first dimension for each coefficient type at each level.

dwtlvm [i - 1]

contains the number of coefficients in the first dimension (for each coefficient type computed) at the (

n_{fwd} - i + 1

)th level of resolution, for

i = 1, 2, \dots, n_{fwd}

. Thus for the first

n_{fwd} - 1

levels of resolution,

dwtlvm [n_{fwd} - i]

is the size of the first dimension of the matrices of vertical, horizontal and diagonal coefficients computed at this level; for the final level of resolution,

dwtlvm [0]

is the size of the first dimension of the matrices of approximation, vertical, horizontal and diagonal coefficients computed.

9: $dwtlvn [nwl]$ – Integer Output

On exit: the number of coefficients in the second dimension for each coefficient type at each level.

dwtlvn [i - 1]

contains the number of coefficients in the second dimension (for each coefficient type computed) at the (

n_{fwd} - i + 1

)th level of resolution, for

i = 1, 2, \dots, n_{fwd}

. Thus for the first

n_{fwd} - 1

levels of resolution,

dwtlvn [n_{fwd} - i]

is the size of the second dimension of the matrices of vertical, horizontal and diagonal coefficients computed at this level; for the final level of resolution,

dwtlvn [0]

is the size of the second dimension of the matrices of approximation, vertical, horizontal and diagonal coefficients computed.

10: $icomm [180]$ – 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 c09abc.

On exit: contains additional information on the computed transform.

11: $fail$ – 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_BAD_PARAM: On entry, argument $⟨ value ⟩$ had an illegal value.
NE_INITIALIZATION: Either the initialization function has not been called first or icomm has been corrupted.

Either the initialization function was called with $wtrans = Nag_SingleLevel$ or icomm has been corrupted.
NE_INT: On entry, $m = ⟨ value ⟩$ .
Constraint: $m = ⟨ value ⟩$ , the value of m on initialization (see c09abc).

On entry, $n = ⟨ value ⟩$ .
Constraint: $n = ⟨ value ⟩$ , the value of n on initialization (see c09abc).

On entry, $nwl = ⟨ value ⟩$ .
Constraint: $nwl \geq 1$ .
NE_INT_2: On entry, $lda = ⟨ value ⟩$ and $m = ⟨ value ⟩$ .
Constraint: $lda \geq m$ .

On entry, $lenc = ⟨ value ⟩$ .
Constraint: $lenc \geq ⟨ value ⟩$ , the total number of coefficents to be generated.

On entry, $nwl = ⟨ value ⟩$ and $nwlmax = ⟨ value ⟩$ in c09abc.
Constraint: $nwl \leq nwlmax$ in c09abc.
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

c09ecc is not threaded in any implementation.

9 Further Comments

The wavelet coefficients at each level can be extracted from the output array c using the information contained in dwtlvm and dwtlvn on exit (see the descriptions of c, dwtlvm and dwtlvn in Section 5). For example, given an input data set,

A

, denoising can be carried out by applying a thresholding operation to the detail (vertical, horizontal and diagonal) coefficients at every level. The elements

c [k_{1}]

c [k_{n_{fwd} + 1} - 1]

, as described in Section 5, contain the detail coefficients,

{\hat{c}}_{i j}

, for

i = n_{fwd}, n_{fwd} - 1, \dots, 1

and

j = 1, 2, \dots, 3 q (i)

, where

q (i)

is the number of each type of coefficient at level

i

and

{\hat{c}}_{i j} = c_{i j} + σ ε_{i j}

and

σ ε_{i j}

is the transformed noise term. If some threshold parameter

α

is chosen, a simple hard thresholding rule can be applied as

{\bar{c}}_{i j} = {\begin{cases} 0, & if ​ | {\hat{c}}_{i j} | \leq α \\ {\hat{c}}_{i j}, & if ​ | {\hat{c}}_{i j} | > α, \end{cases}

taking

{\bar{c}}_{i j}

to be an approximation to the required detail coefficient without noise,

c_{i j}

. The resulting coefficients can then be used as input to c09edc in order to reconstruct the denoised signal. See Section 10 in c09ezc for a simple example of denoising.

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 transform of a dataset using the Daubechies wavelet (see

wavnam = Nag_Daubechies2

in c09abc) using half-point symmetric end extensions, the maximum possible number of levels of resolution, where the number of coefficients in each level and the coefficients themselves are not changed. The original dataset is then reconstructed using c09edc.

c09ec: FL CL CPP AD

NAG CL Interfacec09ecc (dim2_​multi_​fwd)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG CL Interface
c09ecc (dim2_multi_fwd)