tqwt

R2026b

Tunable Q-factor wavelet transform

Syntax

wt = tqwt(x)

wt = tqwt(x,Name=Value)

[wt,info] = tqwt(___)

Description

wt = tqwt(x) returns the tunable Q-factor wavelet transform (TQWT) of x.

• The TQWT is computed to the maximum decomposition level with a quality factor of 1. For more information, see TQWT Decomposition Levels.

• As implemented, the tqwt function uses a redundancy of 3. For more information, see Redundancy.

wt = tqwt(x,Name=Value) specifies one or more additional name-value arguments. For example, wt = tqwt(x,QualityFactor=2) specifies a quality factor of 2.

[wt,info] = tqwt(___) returns the structure array, info, with information about the tunable Q-factor wavelet transform.

Examples

Tunable Q-factor Wavelet Transform of Multisignal

Load a multichannel EEG signal. The signal has 23 channels.

load Espiga3
size(Espiga3,2)

ans = 
23

Obtain the tunable Q-factor wavelet transform of the multisignal to the maximum level using the default quality factor of 1.

wt = tqwt(Espiga3);
numel(wt)

ans = 
12

For 1 ≤ i ≤ numel(wt)-1, the ith element of wt contains the wavelet transform coefficients for the ith subband. The last element of wt contains the lowpass subband coefficients. Confirm the number of columns of any element of wt is equal to the number of channels.

k = 7;
size(wt{k},2)

ans = 
23

Reconstruct the multisignal and demonstrate perfect reconstruction.

xrec = itqwt(wt,size(Espiga3,1));
max(abs(xrec(:)-Espiga3(:)))

ans = 
5.1159e-13

Inspect TQWT Information Structure

Load an ECG signal. Obtain the TQWT of the signal down to level 5 with a quality factor of 2. Also obtain information of the TQWT.

load wecg
lvl = 5;
qf = 2;
[wt,info] = tqwt(wecg,Level=lvl,QualityFactor=qf);

Plot the original signal and compare with the lowpass subband coefficients.

subplot(2,1,1)
plot(wecg)
title("Original Signal")
axis tight
subplot(2,1,2)
plot(wt{end})
title("Lowpass Subband Coefficients")
axis tight

Inspect the TQWT information structure. For each subband, confirm that the ratio of the center frequency to the approximate bandwidth equals the quality factor.

info

info = struct with fields:
    CenterFrequencies: [0.3333 0.2593 0.2016 0.1568 0.1220]
           Bandwidths: [0.1667 0.1296 0.1008 0.0784 0.0610]
                Level: 5
                Alpha: 0.7778
                 Beta: 0.6667

info.CenterFrequencies./info.Bandwidths

ans = 1×5

    2.0000    2.0000    2.0000    2.0000    2.0000

As implemented, the tqwt function uses the redundancy $$r=3$$. Confirm the highpass and lowpass scaling factors, info.Beta and info.Alpha respectively, satisfy the relation $$r=\frac{\beta }{1-\alpha }$$.

info.Beta/(1-info.Alpha)

ans = 
3

Input Arguments

x — Input signal

vector | matrix | 3-D array

Input signal, specified as a single- or double-precision vector, matrix, or 3-D array. If x is a matrix or 3-D array, the TQWT is computed along the columns of x. For 3-D arrays, tqwt interprets the first dimension as time, the second dimension as channels, and the third dimension as a batch.

The TQWT is defined for even-length signals. If the number of samples in x is odd, the last sample of x is repeated to obtain an even-length signal.

Data Types: single | double
Complex Number Support: Yes

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: wt = tqwt(x,Level=3,QualityFactor=2)

Level — Decomposition level

positive integer

Decomposition level of the TQWT, specified as a positive integer between 1 and the maximum level. The maximum level depends on the signal length and quality factor. For more information, see TQWT Decomposition Levels.

Example: wt = tqwt(x,Level=3) specifies a decomposition level of 3.

Data Types: single | double

QualityFactor — Quality factor

1 (default) | positive scalar

Quality factor, specified as a real-valued scalar greater than or equal to 1. The quality factor is the ratio of the center frequency to the bandwidth of the filters. If unspecified, the quality factor defaults to 1.

Example: wt = tqwt(x,QualityFactor=1.5) specifies a quality factor of 1.5.

Data Types: single | double

Output Arguments

wt — Tunable Q-factor wavelet transform

cell array

Tunable Q-factor wavelet transform, returned as a cell array. wt is a cell array with length equal to the maximum level of the TQWT plus one. The ith element of wt contains the TQWT coefficients for the ith subband. The subbands are ordered by decreasing center frequency. The final element of wt contains the lowpass subband coefficients. The wavelet coefficients in wt match x in data type and complexity.

• If x is a row vector, each element of wt is a column vector containing the TQWT coefficients.

• If x is a matrix or 3-D array, the column and page sizes of each element of wt match the column and page sizes of x.

Data Types: single | double

info — Transform information

structure array

Transform information, returned as a structure array. info has five fields:

• CenterFrequencies — The normalized center frequencies (cycles/sample) of the wavelet subbands in the TQWT of x. To convert the frequencies to hertz, multiply CenterFrequencies by the sample rate.

• Bandwidths — The approximate bandwidths of the wavelet subbands in normalized frequency (cycles/sample). To convert the bandwidths to hertz, multiply Bandwidths by the sample rate.

• Level — Level of the TQWT. Note that info.Level may be different from your specified level if you specify a level greater than the maximum supported level for your signal length and quality factor.

• Beta — Highpass scaling factor. The highpass scaling factor is computed from the quality factor as 2/(QualityFactor+1). Accordingly, 0 < Beta ≤ 1.

• Alpha — Lowpass scaling factor. The lowpass scaling factor is computed from the highpass scaling factor as 1-Beta/3. Accordingly, 2/3 ≤ Alpha < 1.

Data Types: struct

More About

TQWT Decomposition Levels

The TQWT minimum and maximum decomposition levels depend on the signal length, N, and quality factor, Q. In the description that follows, the signal length, N, is one sample larger than the input length for odd-length signals.

The maximum decomposition level is

$$\lfloor \log \left(\frac{N}{4Q+4}\right)/\log \left(\frac{3Q+3}{3Q+1}\right)\rfloor ,$$

where the ⌊ ⌋ symbols denote the floor function.

The minimum level also depends on the signal length and quality factor. The logarithm of N, $$\log (N),$$ must satisfy the following inequality:

$$\log (N)\ge \log (4Q+4)-\log (3Q+1)+\log (3Q+3).$$

If $$\log (N)<\log (4Q+4)-\log (3Q+1)+\log (3Q+3),$$ the maximum level is less than 1 and tqwt throws an error.

Redundancy

The TQWT algorithm depends on scaling in the frequency domain:

• lowpass scaling — frequency-domain scaling by α that preserves low-frequency content

• highpass scaling — frequency-domain scaling by β that preserves high-frequency content

The redundancy is defined to be

$$r=\frac{\beta }{1-\alpha }.$$

For more information, see Tunable Q-factor Wavelet Transform.

References

[1] Selesnick, Ivan W. “Wavelet Transform With Tunable Q-Factor.” IEEE Transactions on Signal Processing 59, no. 8 (August 2011): 3560–75. https://doi.org/10.1109/TSP.2011.2143711.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

The tqwt function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

The tqwt function supports GPU array input with these usage notes and limitations:

• The signal x is the only supported input argument.

• The TQWT array wt is the only supported output argument.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2021b

R2026b: Use thread-based environments

You can use tqwt in thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.