By the use of past data to assist i

By the use of past data to assist in measuring (i.e.. quantizingl new data. we leave
ordinary PC and enter the realm of differential PCM (DPCM). In DPCM, a prediction of the next sample value is formed from past values. This prediction can be thought of as instructions for the quantizer to conduct its search for the next sample value in a particular interval. By using the redundancy in the signal to form a prediction. the region of uncertainty is reduced and the quantization can be performed with a reduced number of decisions (or bits) for a given quantization level or with reduced quantization levels for a given number of decisions (or bits). The reduction in redundancy is realized by subtracting the prediction from the next sample value. This difference is called the prediction error.
The quantizing methods described in Section l3.2 are called in instantaneous or
memoryless quantizers because the digital conversion is based on the single (current) input sample. ln Section 13.1 we identified the properties of sources that
permitted source rate reductions. These properties were nonequiprobable source
levels and nonindependent sample values. Instantaneous quantizers achieve
source-coding gains by taking into account the probability density assignment for
each sample. The quantizing methods that take account of sample-to-sample correlation are noninstantaneous quantizers. These quantizers reduce source redundancy by first converting the correlated input sequence into a related sequence
with reduced correlation. reduced variance. or reduced bandwidth. This new sequence is then quantizcd with fewer bits.

Hasil (Inggris) 1:

By the use of past data to assist in measuring (i.e.. quantizingl new data. we leaveordinary PC and enter the realm of differential PCM (DPCM). In DPCM, a prediction of the next sample value is well-formed from past values. This prediction can be thought of as instructions for the quantizer to conduct its search for the next sample value in a particular interval. By using the redundancy in the signal to form a prediction. the region of uncertainty is reduced and the quantization can be performed with a reduced number of decisions (or bits) for a given quantization level or with reduced quantization levels for a given number of decisions (or bits). The reduction in redundancy is realized by subtracting the prediction from the next sample value. This difference is called the prediction error.The quantizing methods described in Section 3.2 l are called in instantaneous ormemoryless quantizers because the digital conversion is based on the single (current) input sample. LN Section 13.1 we identified the properties of sources that' permitted explosives source rate reductions. These properties were nonequiprobable sourcelevels and nonindependent sample values. Achieve Instantaneous quantizerssource-coding gains by taking into account the probability density assignment foreach sample. The quantizing methods that take account of sample-to-sample correlation are noninstantaneous quantizers. These quantizers reduce source redundancy by converting the first correlated input sequence into a related sequencewith reduced correlation. reduced variance. or reduced bandwidth. This new sequence is then quantizcd with fewer bits.