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The section Optimizing Quantization Parameters describes how you can use training data with the lloyds function to help find quantization parameters that will minimize signal distortion. This section describes similar procedures for using the dpcmopt function in conjunction with the two functions dpcmenco and dpcmdeco, which first appear in the previous section.
Note
The training data that you use with dpcmopt should be typical of the kinds of signals that you will actually be quantizing with dpcmenco.
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Example: Comparing Optimized and Nonoptimized DPCM Parameters
This example is similar to the one in the last section. However, whereas the last example created predictor, partition, and codebook in a straightforward but haphazard way, this example uses the same codebook (now called initcodebook) as an initial guess for a new optimized codebook parameter. This example also uses the predictive order, 1, as the desired order of the new optimized predictor. The dpcmopt function creates these optimized parameters, using the sawtooth signal x as training data. The example goes on to quantize the training data itself; in theory, the optimized parameters are suitable for quantizing other data that is similar to x. Notice that the mean square distortion here is much less than the distortion in the previous example.
t = [0:pi/50:2*pi]; x = sawtooth(3*t); % Original signal initcodebook = [-1:.1:1]; % Initial guess at codebook % Optimize parameters, using initial codebook and order 1. [predictor,codebook,partition] = dpcmopt(x,1,initcodebook); % Quantize x using DPCM. encodedx = dpcmenco(x,codebook,partition,predictor); % Try to recover x from the modulated signal. decodedx = dpcmdeco(encodedx,codebook,predictor); distor = sum((x-decodedx).^2)/length(x) % Mean square error distor = 0.0063
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