Digital Signal Processing Reference
In-Depth Information
speech after decoding and quantization error, respectively. We can see that the quantization error
follows the amplitude of speech data relatively. The decoded speech sounds no different when
compared with the original speech. Programs 11.8 to 11.10 in Section 11.7 show the details of the
MATLAB implementation.
11.3 EXAMPLES OF DIFFERENTIAL PULSE CODE MODULATION (DPCM),
DELTA MODULATION, AND ADAPTIVE DPCM G.721
Data compression can be further achieved using differential pulse code modulation (DPCM). The
general idea is to use past recovered values as the basis to predict the current input data and then
encode the difference between the current input and the predicted input. Since the difference has
a significantly reduced signal dynamic range, it can be encoded with fewer bits per sample. Therefore,
we obtain data compression. First, we study the principles of the DPCM concept that will help us
understand adaptive DPCM in the next subsection.
11.3.1 Examples of Differential Pulse Code Modulation and Delta Modulation
Figure 11.11 shows a schematic diagram for the DPCM encoder and decoder. We denote the original
signal xðnÞ ; the predicted signal xðnÞ ; the quantized or recovered signal xðnÞ ; the difference signal to be
quantized dðnÞ ; and the quantized difference signal d q ðnÞ . The quantizer can be chosen as a uniform
quantizer, a midtread quantizer (e.g., see Table 11.5 ) , or any others available. The encoding block
produces a binary bit stream in the DPCM encoding. The predictor uses the past predicted signal and
quantized difference signal to predict the current input value xðnÞ as close as possible. The digital filter
or adaptive filter can serve as the predictor. On the other hand, the decoder recovers the quantized
difference signal, which can be added to the predictor output signal to produce the quantized and
recovered signal, as shown in Figure 11.11 ( b).
(a)
(b)
FIGURE 11.11
DPCM block diagram: (a) encoder; (b) decoder.
 
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