Lecture 7 probabilidad de error de transmisión pcm. formateo de señales dpcm, adpcm.
PCM & DPCM & DM - Sharifce.sharif.edu/courses/93-94/2/ce967-1/resources... · 20 Adaptive PCM and...
Transcript of PCM & DPCM & DM - Sharifce.sharif.edu/courses/93-94/2/ce967-1/resources... · 20 Adaptive PCM and...
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PCM & DPCM & DM
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Pulse-Code Modulation (PCM) :
In PCM each sample of the signal is quantized to one of the amplitude levels, where B is the number of bits used to represent each sample.
The rate from the source is bps.
The quantized waveform is modeled as :
q(n) represent the quantization error, Which we treat as an additive noise.
B2
sBF
)()()(~ nqnsns
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Pulse-Code Modulation (PCM) :
The quantization noise is characterized as a
realization of a stationary random process q in
which each of the random variables q(n) has
uniform pdf.
Where the step size of the quantizer is22
q
B 2
2
/1
2
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Pulse-Code Modulation (PCM) :
If :maximum amplitude of signal,
The mean square value of the quantization
error is :
Measure in dB, The mean square value of the
noise is :
B
A
2
max
maxA
122
A
12
Δ|(n)q
3Δ
1
(n)dqqΔ
1 (n)q
2B
2
max
2Δ/2
Δ/2
3
Δ/2
Δ/2
22
.dB 8.10612
2log10
12log10
2
10
2
10
BB
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Pulse-Code Modulation (PCM) : The quantization noise decreases by 6 dB/bit.
If the headroom factor is h, then
The signal to noise (S/N) ratio is given by
(Amax=1)
In dB, this is
hh
AX
B
rms
2max
2
2
2
22
1212/
SNRh
X
N
S B
rms
hBh
B
102
2
10dB log208.106212
log10SNR
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Pulse-Code Modulation (PCM) :
Example :
We require an S/N ratio of 60 dB and that a
headroom factor of 4 is acceptable. Then the
required word length is :
60=10.8 + 6B – 20
If we sample at 8 KHZ, then PCM require
bit 112.10 B
4log10
bit/s. 8800011 8 k
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Pulse-Code Modulation (PCM) :
A nonuniform quantizer characteristic is
usually obtained by passing the signal
through a nonlinear device that compress
the signal amplitude, follow by a uniform
quantizer.
Compressor A/D D/A Expander
Compander
(Compressor-Expander)
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Companding: Compression and Expanding
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Original Signal
After Compressing, Before Expanding
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Companding
A logarithmic compressor employed in
North American telecommunications
systems has input-output magnitude
characteristic of the form
is a parameter that is selected to give the
desired compression characteristic.
)1log(
|)|1log(||
sy
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Companding
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Companding
The logarithmic compressor used in
European telecommunications system is
called A-law and is defined as
A
sAy
log1
|)|1log(||
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Companding
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DPCM :
A Sampled sequence u(m), m=0 to m=n-1.
Let be the value of the
reproduced (decoded) sequence.
),...2(~),1(~ nunu
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DPCM:
At m=n, when u(n) arrives, a quantify ,
an estimate of u(n), is predicted from the
previously decoded samples
i.e.,
”prediction rule”
Prediction error:
)(~ nu
),...2(~),1(~ nunu
),...);2(~),1(~()(~ nununu
)(~)()( nunune
:(.)
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DPCM :
If is the quantized value of e(n), then
the reproduced value of u(n) is:
Note:
)(~ ne
)(~)(~)(~ nenunu
)(in error on Quantizati The :)(
)(~)(
))(~)(~())()(~()(~)(
)()(~)(
nenq
nene
nenunenununu
nenunu
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DPCM CODEC:
)(~ nu
)(~ nuΣ Quantizer
Σ
ΣCommunication
Channel
PredictorPredictor
)(nu )(ne )(~ ne
)(~ nu
)(~ nu
)(~ ne
Coder Decoder
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DPCM:
Remarks:
The pointwise coding error in the input
sequence is exactly equal to q(n), the
quantization error in e(n).
With a reasonable predictor the mean
sequare value of the differential signal e(n) is
much smaller than that of u(n).
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DPCM:
Conclusion:
For the same mean square quantization error,
e(n) requires fewer quantization bits than u(n).
The number of bits required for transmission
has been reduced while the quantization error
is kept the same.
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DPCM modified by the addition of
linearly filtered error sequence
)(~ nu
)(~ nuΣ Quantizer
Σ
Communication
Channel
Linear filter
)(nu )(ne )(~ ne
)(~ nu
)(~ nu
)(~ ne
Coder Decoder
(i)} a{
Linear filter
(i)} b{
Σ
Linear
filter
(i)} a{
Linear
filter(i)} b{
Σ
Σ
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Adaptive PCM and Adaptive DPCM
Speech signals are quasi-stationary in nature
The variance and the autocorrelation function of the source output vary
slowly with time.
PCM and DPCM assume that the source output is stationary.
The efficiency and performance of these encoders can be improved
by adaptation to the slowly time-variant statistics of the speech
signal.
Adaptive quantizer
feedforward
feedbackward
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Example of quantizer with an
adaptive step size
∆ 2∆ 3∆-∆-2∆-3∆
∆/2
3∆/2
5∆/2
7∆/2
-∆/2
-3∆/2
-5∆/2
-7∆/2
M (1)
M (2)
M (3)
M (4)
M (1)
M (2)
M (3)
M (4)
000
001
010
011 0
100
101
110
111 Previous Output
Multiplier
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ADPCM with adaptation of the predictor
)(~ nu
)(~ nuΣ Quantizer
Σ
ΣCommunication
Channel
Predictor
Predictor
)(nu )(ne )(~ ne
)(~ nu
)(~ ne
Coder Decoder
DecoderEncoder
Step-size
adaptation
Predictor
adaptation
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Delta Modulation : (DM)
Predictor : one-step delay function
Quantizer : 1-bit quantizer
)1(~)()(
)1(~)(~
nunune
nunu
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Delta Modulation : (DM)
Primary Limitation of DM
Slope overload : large jump region
Max. slope = (step size)X(sampling freq.)
Granularity Noise : almost constant region
Instability to channel noise
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DM:
Unit Delay
Unit Delay
Integrator
)(nu )(ne )(~ ne
)(~ nu)(~ nu
)(~ ne )(~ nu
)(~ nu
Coder
Decoder
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DM:
Step size effect :
Step Size (i) slope overload
(sampling frequency ) (ii) granular Noise
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Adaptive DM:
1kX
1kE1ks
Adaptive
Function
Unit DelaykX 1k
Stored
k mink ,E,
11
min1min
min11
11
|| if
|| if ]2
[||
][sgn
kkk
kk
kk
kkk
kKk
XX
E
EE
XSE
This adaptive approach simultaneously minimizes the effects of both
slope overload and granular noise
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Vector Quantization
(VQ)
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Vector Quantization :
Quantization is the process of
approximating continuous amplitude
signals by discrete symbols.
Partitioning of
two-dimensional
Space into 16 cells.
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Vector Quantization :
The LBG algorithm first computes a 1-
vector codebook, then uses a splitting
algorithm on the codeword to obtain the
initial 2-vector codebook, and continue the
splitting process until the desired M-vector
codebook is obtained.
This algorithm is known as the LBG
algorithm proposed by Linde, Buzo and
Gray.
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Vector Quantization : The LBG Algorithm :
Step 1: Set M (number of partitions or cells)=1.Find the centroid of all the training data.
Step 2: Split M into 2M partitions by splitting each current codeword by finding two points that are far apart in each partition using a heuristic method, and use these two points as the new centroids for the new 2M codebook. Now set M=2M.
Step 3: Now use a iterative algorithm to reach the best set of centroids for the new codebook.
Step 4: if M equals the VQ codebook size require, STOP; otherwise go to Step 2.