Design vector quantizer using Vector Quantizer Design Tool (VQDTool)
Quantizers
dspquant2
Double-click on the Vector Quantizer Design block to start VQDTool,
a GUI that allows you to design and implement a vector quantizer.
You can also start VQDTool by typing vqdtool
at
the MATLAB^{®} command prompt. Based on your specifications, VQDTool
iteratively calculates the codebook values that minimize the mean
squared error between the training set and the codebook until the
stopping criteria for the design process is satisfied. The block uses
the resulting codebook values to implement your vector quantizer.
For the Training Set parameter, enter a k-by-M matrix
of values you want to use to train the quantizer codebook. The variable
k, where $$k\ge 1$$, is the length of each training vector. It also
represents the dimension of your quantizer. The variable M,
where $$M\ge 2$$, is the number of training vectors. This data can
be created using a MATLAB function, such as the default value randn(10,1000)
,
or it can be any variable defined in the MATLAB workspace.
You have two choices for the Source of initial codebook parameter.
Select Auto-generate
to have the block
choose the values of the initial codebook. In this case, the block
picks N random training vectors as the initial
codebook, where N is the Number of levels parameter
and $$N\ge 2$$. When you select User defined
,
enter the initial codebook values in the Initial codebook field.
The initial codebook matrix must have the same number of rows as the
training set. Each column of the codebook is a codeword, and your
codebook must have at least two codewords.
For the given training set and initial codebook, the block performs an iterative process, using the Generalized Lloyd Algorithm (GLA), to design a final codebook. For each iteration of the GLA, the block first associates each training vector with its nearest codeword by calculating the distortion. You can specify one of the two possible methods for calculating distortion using the Distortion measure parameter.
When you select Squared error
for
the Distortion measure parameter, the block finds
the nearest codeword by calculating the squared error (unweighted).
Consider the codebook $$CB=\left[\begin{array}{cccc}C{W}_{1}& C{W}_{2}& \mathrm{...}& C{W}_{N}\end{array}\right]$$. This codebook has N codewords;
each codeword has k elements. The i-th
codeword is defined as $$C{W}_{i}=\left[\begin{array}{cccc}{a}_{1i}& {a}_{2i}& \mathrm{...}& {a}_{ki}\end{array}\right]$$. The training set has M columns
and is defined as $$U=\left[\begin{array}{cccc}{U}_{1}& {U}_{2}& \mathrm{...}& {U}_{M}\end{array}\right]$$, where the p-th training vector
is $${U}_{p}={\left[\begin{array}{cccc}{u}_{1p}& {u}_{2p}& \mathrm{...}& {u}_{kp}\end{array}\right]}^{\prime}$$. The squared error (unweighted) is calculated using
the equation
$$D={\displaystyle \sum _{j=1}^{k}{\left({a}_{ji}-{u}_{jp}\right)}^{2}}$$
When
you select Weighted squared error
for the Distortion
measure parameter, enter a vector or matrix for the Weighting
factor parameter. When the weighting factor is a vector,
its length must be equal to the number of rows in the training set.
This weighting factor is used for each training vector. When the weighting
factor is a matrix, it must be the same size as the training set matrix.
The block finds the nearest codeword by calculating the weighted squared
error. If the weighting factor for the p-th column
of the training vector, $${U}_{p}$$, is defined as $$Wp={\left[\begin{array}{cccc}{w}_{1p}& {w}_{2p}& \mathrm{...}& {w}_{kp}\end{array}\right]}^{\prime}$$, then the weighted squared error is defined by the
equation
$$D={\displaystyle \sum _{j=1}^{k}{w}_{jp}{\left({a}_{ji}-{u}_{jp}\right)}^{2}}$$
Once the block has associated all the training vectors with their nearest codeword vectors, the block calculates the mean squared error for the codebook and checks to see if the stopping criteria for the process has been satisfied.
The two possible options for the Stopping criteria parameter
are Relative threshold
and Maximum
iteration
. When you want the design process to stop
when the fractional drop in the squared error is below a certain value,
select Relative threshold
. Then, type the
maximum acceptable fractional drop in the Relative threshold field.
The fraction drop in the squared error is defined as
$$\frac{\text{erroratpreviousiteration}-\text{erroratcurrentiteration}}{\text{erroratpreviousiteration}}$$
When
you want the design process to stop after a certain number of iterations,
choose Maximum iteration
. Then, enter the
maximum number of iterations you want the block to perform in the Maximum
iteration field. For Stopping criteria,
you can also choose Whichever comes first
and
enter Relative threshold and Maximum
iteration values. The block stops iterating as soon as
one of these conditions is satisfied.
When a training vector has the same distortion for two different
codeword vectors, the algorithm uses the Tie-breaking rule parameter
to determine which codeword vector the training vector is associated
with. When you want the training vector to be associated with the
lower indexed codeword, select Lower indexed codeword
.
To associate the training vector with the higher indexed codeword,
select Higher indexed codeword
.
With each iteration, the block updates the codeword values in order to minimize the distortion. The Codebook update method parameter defines the way the block calculates these new codebook values.
Note
If, for the Distortion measure parameter,
you choose |
If, for the Distortion measure parameter,
you choose Weighted squared error
and you
choose Mean
for the Codebook
update method parameter, the new codeword vector is found
as follows. Suppose there are three training vectors associated with
one codeword vector. The training vectors are
$$T{S}_{1}=\left[\begin{array}{c}1\\ 2\end{array}\right],\text{}T{S}_{3}=\left[\begin{array}{c}10\\ 12\end{array}\right],\text{and}T{S}_{7}=\left[\begin{array}{c}11\\ 12\end{array}\right].$$
The new codeword vector is calculated as $$C{W}_{new}=\left[\begin{array}{c}\frac{1+10+11}{3}\\ \frac{2+12+12}{3}\end{array}\right]$$
where the denominator is the number of training vectors associated
with this codeword. If, for the Codebook update method parameter,
you choose Centroid
and you specify the
weighting factors $${W}_{1}=\left[\begin{array}{c}0.1\\ 0.2\end{array}\right]$$, $${W}_{3}=\left[\begin{array}{c}1\\ 0.6\end{array}\right]$$, and $${W}_{7}=\left[\begin{array}{c}0.3\\ 0.4\end{array}\right]$$, the new codeword vector is calculated as
$$C{W}_{new}=\left[\begin{array}{c}\frac{\left(0.1\right)\left(1\right)+\left(1\right)\left(10\right)+\left(0.3\right)\left(11\right)}{0.1+1+0.3}\\ \frac{\left(0.2\right)\left(2\right)+\left(0.6\right)\left(12\right)+\left(0.4\right)\left(12\right)}{0.2+0.6+0.4}\end{array}\right]$$
Click Design and Plot to design the quantizer with the parameter values specified on the left side of the GUI. The performance curve and the entropy of the quantizer are updated and displayed in the figures on the right side of the GUI.
Note You must click Design and Plot to apply any changes you make to the parameter values in the VQDTool dialog box. |
The following is an example of how the block calculates the entropy of the quantizer at each iteration. Suppose you have a codebook with four codewords and a training set with 200 training vectors. Also suppose that, at the i-th iteration, 40 training vectors are associated with the first codeword, 60 training vectors are associated with the second codeword, 20 training vectors are associated with the third codeword, and 80 training vectors are associated with the fourth codeword. The probability that a training vector is associated with the first codeword is $$\frac{40}{200}$$. The probabilities that training vectors are associated with the second, third, and fourth codewords are $$\frac{60}{200}$$, $$\frac{20}{200}$$, and $$\frac{80}{200}$$, respectively. The GUI uses these probabilities to calculate the entropy according to the equation
$$H={\displaystyle \sum _{i=1}^{N}-{p}_{i}{\mathrm{log}}_{2}{p}_{i}}$$
where N is the number of codewords. Based on these probabilities, the GUI calculates the entropy of the quantizer at the i-th iteration as
$$\begin{array}{l}H=-\left(\frac{40}{200}{\mathrm{log}}_{2}\frac{40}{200}+\frac{60}{200}{\mathrm{log}}_{2}\frac{60}{200}+\frac{20}{200}{\mathrm{log}}_{2}\frac{20}{200}+\frac{80}{200}{\mathrm{log}}_{2}\frac{80}{200}\right)\\ H=1.8464\end{array}$$
VQDTool can export parameter values that correspond to the figures displayed in the GUI. Click the Export Outputs button, or press Ctrl+E, to export the Final Codebook, Mean Square Error, and Entropy values to the workspace, a text file, or a MAT-file.
In the Model section of the GUI,
specify the destination of the block that will contain the parameters
of your quantizer. For Destination, select Current
model
to create a block with your parameters in the
model you most recently selected. Type gcs
in the MATLAB Command
Window to display the name of your current model. Select New
model
to create a block in a new model file.
From the Block type list, select Encoder
to
design a Vector Quantizer Encoder block. Select Decoder
to
design a Vector Quantizer Decoder block. Select Both
to
design a Vector Quantizer Encoder block and a Vector Quantizer Decoder
block.
In the Encoder block name field, enter a name for the Vector Quantizer Encoder block. In the Decoder block name field, enter a name for the Vector Quantizer Decoder block. When you have a Vector Quantizer Encoder and/or Decoder block in your destination model with the same name, select the Overwrite target block check box to replace the block's parameters with the current parameters. When you do not select this check box, a new Vector Quantizer Encoder and/or Decoder block is created in your destination model.
Click Generate Model. VQDTool uses the parameters that correspond to the current plots to set the parameters of the Vector Quantizer Encoder and/or Decoder blocks.
Enter the samples of the signal you would like to quantize.
This data set can be a MATLAB function or a variable defined
in the MATLAB workspace. The typical length of this data vector
is 1e5
.
Select Auto-generate
to have the
block choose the initial codebook values. Choose User
defined
to enter your own initial codebook values.
Enter the number of codeword vectors, N, in your codebook matrix, where N ≥ 2.
Enter your initial codebook values. From the Source
of initial codebook list, select User defined
in
order to activate this parameter. The codebook must have the same
number of rows as the training set. You must provide at least two
codeword vectors.
When you select Squared error
, the
block finds the nearest codeword by calculating the squared error
(unweighted). When you select Weighted squared error
,
the block finds the nearest codeword by calculating the weighted squared
error.
Enter a vector or matrix. The block uses these values to compute the weighted squared error. When the weighting factor is a vector, its length must be equal to the number of rows in the training set. This weighting factor is used for each training vector. When the weighting factor is a matrix, it must be the same size as the training set matrix. The individual weighting factors cannot be negative. The weighting factor vector or matrix cannot contain all zeros.
Choose Relative threshold
to enter
the maximum acceptable fractional drop in the squared quantization
error. Choose Maximum iteration
to specify
the number of iterations at which to stop. Choose Whichever
comes first
and the block stops the iteration process
as soon as the relative threshold or maximum iteration value is attained.
This parameter is available when you choose Relative
threshold
or Whichever comes first
for
the Stopping criteria parameter. Enter the value
that is the maximum acceptable fractional drop in the squared quantization
error.
This parameter is available when you choose Maximum
iteration
or Whichever comes first
for
the Stopping criteria parameter. Enter the maximum
number of iterations you want the block to perform.
When a training vector has the same distortion for two different
codeword vectors, select Lower indexed codeword
to
associate the training vector with the lower indexed codeword. Select Higher
indexed codeword
to associate the training vector with
the lower indexed codeword.
When you choose Mean
, the new codeword
vector is calculated by taking the average of all the training vector
values that were associated with the original codeword vector. When
you choose Centroid
, the block calculates
the new codeword vector by taking the weighted average of all the
training vector values that were associated with the original codeword
vector Note that if, for the Distortion measure parameter,
you choose Squared error
, the Codebook
update method parameter is set to Mean
.
Choose Current model
to create a
Vector Quantizer block in the model you most recently selected. Type gcs
in
the MATLAB Command Window to display the name of your current
model. Choose New model
to create a block
in a new model file.
Select Encoder
to design a Vector
Quantizer Encoder block. Select Decoder
to
design a Vector Quantizer Decoder block. Select Both
to
design a Vector Quantizer Encoder block and a Vector Quantizer Decoder
block.
Enter a name for the Vector Quantizer Encoder block.
Enter a name for the Vector Quantizer Decoder block.
When you do not select this check box and a Vector Quantizer Encoder and/or Decoder block with the same block name exists in the destination model, a new Vector Quantizer Encoder and/or Decoder block is created in the destination model. When you select this check box and a Vector Quantizer Encoder and/or Decoder block with the same block name exists in the destination model, the parameters of these blocks are overwritten by new parameters.
Click this button and VQDTool uses the parameters that correspond to the current plots to set the parameters of the Vector Quantizer Encoder and/or Decoder blocks.
Click this button to design a quantizer using the parameters on the left side of the GUI and to update the performance curve and entropy plots on the right side of the GUI.
You must click Design and Plot to apply any changes you make to the parameter values in the VQDTool GUI.
Click this button, or press Ctrl+E, to export the Final Codebook, Mean Squared Error, and Entropy values to the workspace, a text file, or a MAT-file.
Gersho, A. and R. Gray. Vector Quantization and Signal Compression. Boston: Kluwer Academic Publishers, 1992.
Double-precision floating point
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