Databases Reference
In-Depth Information
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F I GU R E 11 . 9
The reconstructed sequence using a third-order predictor and an
eight-level uniform quantizer.
these issues are specific to speech coding, and we will encounter them when we specifically
discuss encoding speech using DPCM. However, the problem is also much more widespread
than when encoding speech. A general response to the nonstationarity of the input is the use
of adaptation in prediction. We will look at some of these approaches in the next section.
11.5 Adaptive DPCM
As DPCM consists of two main components, the quantizer and the predictor, making DPCM
adaptive means making the quantizer and the predictor adaptive. Recall that we can adapt a
system based on its input or output. The former approach is called forward adaptation; the
latter, backward adaptation. In the case of forward adaptation, the parameters of the system are
updated based on the input to the encoder, which is not available to the decoder. Therefore, the
updated parameters have to be sent to the decoder as side information. In the case of backward
adaptation, the adaptation is based on the output of the encoder. As this output is also available
to the decoder, there is no need for transmission of side information.
In cases where the predictor is adaptive, especially when it is backward adaptive, we
generally use adaptive quantizers (forward or backward). The reason for this is that the
backward adaptive predictor is adapted based on the quantized outputs. If for some reason
the predictor does not adapt properly at some point, this results in predictions that are far
from the input, and the residuals will be large. In a fixed quantizer, these large residuals will
tend to fall in the overload regions with consequently unbounded quantization errors. The
reconstructed values with these large errors will then be used to adapt the predictor, which will
result in the predictor moving further and further from the input.
The same constraint is not present for quantization, and we can have adaptive quantization
with fixed predictors.
