| Communications Toolbox | |
Optimizing Quantization Parameters
Quantization distorts a signal. You can lessen the distortion by choosing appropriate partition and codebook parameters. However, testing and selecting parameters for large signal sets with a fine quantization scheme can be tedious. One way to produce partition and codebook parameters easily is to optimize them according to a set of so-called training data.
| Note The training data that you use should be typical of the kinds of signals that you will actually be quantizing. |
Example: Optimizing Scalar Quantization Parameters
The lloyds function optimizes the partition and codebook according to the Lloyd algorithm. The code below optimizes the partition and codebook for one period of a sinusoidal signal, starting from a rough initial guess. Then it uses these parameters to quantize the original signal using the initial guess parameters as well as the optimized parameters. The output shows that the mean square distortion after quantizing is much less for the optimized parameters. Notice that the quantiz function automatically computes the mean square distortion and returns it as the third output parameter.
% Start with the setup from 2nd example in "Quantizing a Signal." t = [0:.1:2*pi]; sig = sin(t); partition = [-1:.2:1]; codebook = [-1.2:.2:1]; % Now optimize, using codebook as an initial guess.[partition2,codebook2]=lloyds(sig,codebook);[index,quants,distor]=quantiz(sig,partition,codebook);[index2,quant2,distor2] = quantiz(sig,partition2,codebook2); % Compare mean square distortions from initial and optimized [distor, distor2] % parameters. ans = 0.0148 0.0024
| | Quantizing a Signal | Implementing Differential Pulse Code Modulation | |