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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.
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.
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Oleh penggunaan data masa lalu untuk membantu mengukur (yaitu. quantizingl baru data. kita meninggalkanPC biasa dan memasuki wilayah diferensial PCM (DPCM). Dalam DPCM, ramalan nilai sampel berikutnya terbentuk dari nilai-nilai masa lalu. Prediksi ini dapat dianggap sebagai petunjuk untuk quantizer untuk melakukan yang mencari nilai sampel berikutnya dalam interval tertentu. Dengan menggunakan redundansi dalam sinyal untuk membentuk prediksi. wilayah ketidakpastian berkurang dan kuantisasi dapat dilakukan dengan berkurangnya jumlah keputusan (atau bit) untuk tingkat tertentu kuantisasi atau dengan kuantisasi mengurangi tingkat untuk nomor tertentu keputusan (atau bit). Pengurangan redundansi diwujudkan dengan mengurangi prediksi dari nilai sampel berikutnya. Perbedaan ini disebut kesalahan prediksi.Quantizing metode yang dijelaskan dalam bagian l3.2 disebut dalam seketika ataumemoryless quantizers karena konversi digital didasarkan pada satu sampel masukan (saat ini). LN bagian 13.1 kami identified sifat dari sumber yangdiijinkan pengurangan tingkat sumber. Properti ini adalah sumber nonequiprobabletingkat dan nilai-nilai nonindependent sampel. Seketika quantizers mencapaikode sumber keuntungan dengan memperhatikan penetapan kepekatan probabilitassetiap sampel. Metode quantizing yang memperhitungkan korelasi sampel untuk sampel adalah noninstantaneous quantizers. Quantizers ini mengurangi redundansi sumber dengan posisi mengubah urutan input berkorelasi ke urutan terkaitdengan mengurangi korelasi. varians berkurang. atau mengurangi bandwidth. Urutan ini baru ini kemudian quantizcd dengan lebih sedikit bit.
Sedang diterjemahkan, harap tunggu..
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.
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.
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