Image Compression using DPCM with LMS Algorithm

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 824
Image Compression using DPCM with LMS Algorithm
Reenu Sharma, Abhay Khedkar
SRCEM, Banmore
-----------------------------------------------------------------****---------------------------------------------------------------
Abstract: The Differential pulse code modulation (DPCM) [1]
may be used to remove the unused bit in the image for image
compression. In this paper we compare the compressed image
for 1, 2, 3, bit and also compare the estimation error. The LMS
[2] Algorithm may be used to adapt the coefficients of an
adaptive prediction filter for image source coding. In the
method used in this paper we decrease the compressed image
distortion and also the estimation error. The estimation error is
reduced as much as 7-8 dB using DPCM with LMS Algorithm.
Key Words: - Adaptive filter, LMS algorithm, DPCM,
Quantization.
1. INTRODUCTION
In a communication environment, the difference
between adjacent time samples for image is small,
coding techniques have envolved based on transmitting
sample-to-sample differences rather than actual sample
value. Successive differences are in fact a special case of
a class of non-instantaneous converters called N-tap
linear predictive coders. These coders, sometimes
called predictor-corrector coders, predict the next input
sample value based on the previous input sample
values. This structure is shown in figure 1. In this type
of converter, the encoder forms the prediction error (or
the residue) as the difference between the next
measured sample value and the predicted sample value.
The equation for the prediction error [3] is
1.1
In figure 1: Where Q=Quantizer, is the nth input
sample, is the predicted value, and is the
associated prediction error. This is performed in the
predict-and-compare loop, the loop shown in figure 1.
It’s prediction by forming the sum of its prediction and
the prediction error
1.2
Where quant (.) represents the quantization operation,
is the quantization [4] version of the prediction
error, and is the corrected and quantized version
of the input sample. This is performed in the predict-
and-correct loop.
∑ Q
LMS
Predictor ∑
+
+
Predict and compare loop
Predict and correct loop +
Figure 1 Basic Block diagram of DPCM with LMS
Algorithm image compression system

Figure 2 Original image
The communication task is that of transmitting the
difference (the error signal) between the prediction and
the actual data sample. For this reason, this class of
coder is often called a differential pulse code modulator
(DPCM) [3]. If the prediction model forms predictions
that are close to the actual sample values, the residues
variance (relative to the original signal).
2. ANALYSIS OF DPCM
In DPCM [3] we transmit not the present sample x(n),
but e(n) (the difference between x(n) and its predicted
value y(n)). At the receiver, we generate y(n) from the
past sample value to which the received x(n) is added to
generate x(n). There is, however, one difficulty
associated with this scheme. At the receiver, instead of
the past samples as well as we
have their quantized version this
will increase the error in reconstruction. In such a case,
a better strategy is to determine the estimate of
(instead of ), at the transmitter also from the
quantized samples difference
e(n)=x(n)-y(n) is now transmitted via PCM. At the
receiver, we can generate and from the received
we can reconstruct Figure 1 shown a DPCM
predictor. We shall soon show that the predictor input
is Naturally, its output is the predicted value
of The difference of original image data, and
prediction image data, is called estimation
residual, . So
2.1
is quantized to yield
Where is the quantization error,
quantized signal. And
2.2
The prediction output is fed back to its input so
that the predictor input is
2.3
This shows is quantized version of The
prediction input is indeed , as assumed. The
quantized signal is now transmitted over the
channel.
3. IMAGE COMPRESSION USING DPCM AND
LMS ALGORITHM
A block diagram of the LMS adaptive image
compression system is shown in figure 1. It is seen that
the image prediction is formed in a linear manner
at the output of the LMS filter:
50 100 150 200 250
50
100
150
200
250

3.1
3.2
In equation 3.2, the are N adaptive predictor
coefficients, the are the reconstructed image data,
and k is 1, 2……….N integer values which select the
previous image pixel on which base the current
prediction. At each scanned pixel a prediction residual
(error), is computed
3.4
This quantized residual is send to the receiver. The
quantization residual is determine
3.5
This residual is then quantized to form and the
quantized residual is also used to update the predictor
coefficient for the next iteration by the well known least
mean squares (LMS) [5] algorithm.
3.6
The parameter µ is known as the step size parameter
and is a small positive constant, which control steady-
state and convergent mean-square residual
characteristics of the predictor. The LMS algorithm is an
approximation to the gradient search method for
iteratively computing the N optimal coefficients
which minimize the mean square prediction residual. It
is known by [6] that the error between the original
image and the reconstructed image at the receiver is
simply the quantization error Thus, the distortion
between the original discrete image x(n) and the
reconstructed value y(n) at the receiver is given by
3.7
(Assuming the no channel-induced errors)
Therefore, if the goal of the system is an accurate
reconstruction of the image, then an algorithm is
desired which will form an accurate so that e(n)
will have smaller variance and the quantizer levels may
be adjusted to give a smaller quantization error.
Hence, a lower reconstruction error, or distortion, will
be present at the receiver. The quantizer levels
themselves may be fixed or may vary as some function
of the residual sequence . Although, in general, the
position of the quantizer levels could be adaptive, for
simplicity, in this correspondence we only examine the
case of a quantizer with fixed levels.
Alternatively, if the goal of the system is to reduce the
bit rate over the channel subject to some distortion
criteria, then we may reduce the number of quantizer
levels which span the residual signal range and, hence,
produce shorter code words per level. In this situation
the LMS adaptive predictor reduces the average
number of bits per image while maintaining an
acceptable visual appearance at the receiver.
4. SIMULATION RESULT
In this paper we use 256×256 image in figure 2 were
used in experimental work to illustrate the
performance advantages of using LMS [7] as an
adaptive predictor. The image of figure 2 was processed
with the residual quantizer consistiting of b=1, 2 and 3
bits (2, 4 and 8 quantization levels respectively) the

DPCM image quantization [8], [9]. The dynamic range of
data was eight bits from grey level 0 to 255. The figure
3 plots the average square distortion versus
transmitted bit rate for the woman image. All values of
average square error are in dB referenced to the
performance of the 1bits/pixel fixed coefficient
predictor. The bit rate is in bits/pixel and is controlled
by the number of levels in the quantizer. If number of
bit increasing and distortion will be decrease. Figure 7,
8, and 9 is shown the prediction mean square versus
gray level respectively for 1, 2, and 3 bits reconstructed
image. And figure 10 is shown the comparison of PMSE.
If the number of bits is increasing then PMSE will be
decreasing.
Table 1 Condition in Simulation Experiment
Image Matrix size 256×256
No of Filter Taps 110
No of Bits 1, 2, 3 bits
Quantization level 2, 4, 8 quantizer
LMS Parameter
1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3
-23.25
-23.2
-23.15
-23.1
-23.05
-23
-22.95
-22.9
Average square distortion versus transmitted bit rate
AverageSquareDistortion[dB]
bit/pixel
LMS
Figure 3 average square distortions versus transmitted
bit rate.
50 100 150 200 250
50
100
150
200
250
50 100 150 200 250
50
100
150
200
250
50 100 150 200 250
50
100
150
200
250
Figure 4 1bits/pixel LMS images

0 50 100 150 200 250 300
-46
-44
-42
-40
-38
-36
-34
-32
-30
-28
-26
PMSE of LMS Algorithm
PMSE[dB]
sample number
1bit
Figure 7 PMSE [dB] versus Sample number for
1bits/sample
0 50 100 150 200 250 300
-65
-60
-55
-50
-45
-40
-35
-30
PMSE[dB]
sample number
2bit
2bits/sample.
0 50 100 150 200 250 300
-75
-70
-65
-60
-55
-50
-45
-40
-35
-30
PMSE[dB]
sample number
3bit
3bits/sample.
0 50 100 150 200 250 300
-75
-70
-65
-60
-55
-50
-45
-40
-35
-30
-25
Comparision of PMSE For 1,2,3 Bits
PMSE[dB]
sample number
1bit
2bit
3bit
Figure 10 PMSE [dB] versus Sample number for 1, 2, 3
bits/sample comparison.
5. CONCLUSSION
The LMS is a simple and robust adaptive algorithm and
DPCM use the LMS for prediction. At last the distortion is
reduce for 1, 2, 3 bits and also reduce the estimation
mean square error. The distortion and the estimation
mean square error is very less. We compare the
estimation mean square error in dB. This difference is 7-
9 dB respectively for 1, 2, 3 bits as shown in figure 10
and the reduce image shown in figure 4, 5, and 6
respectively this work carried out in future also.
REFERENCES
[1]. A. Habbi, “Comparison of Nth-order DPCM encoder
with linear transformation and block quantization
techniques,” IEEE Trans. Commun., vol. COM-19, pp. 948-
956, Dec. 1971.
[2]. S.Haykin and T.Kailath “Adaptive Filter Theory”
Fourth Edition. Prentice Hall, Pearson Eduaction 2002.
[3]. B. P. Lathi and Zhi ding “Modern Digital and Analog
Communication Systems” International Fourth Edition.
New York Oxford University Press-2010, pp.292.
[4]. J. E. Modestino, and D. G. Daut, “Source-channel
coding of images,” IEEE Trans. Commun., vol. COM-27,
pp. 1644-1659, Nov. 1979.
[5]. J. R. Zeidler et al., “Adaptive enhancement of
mulyiple sinusoids in uncorrelated noise,” IEEE Trans.

Acoust., Speech, Signal Processing, vol. ASSP-26, pp. 240-
254, June 1978.
[6]. J. G. Prokis, Digital Communications. New York:
McGraw-Hill, 1983.
[7]. S. T. Alexander and S. A. Rajala, “Analysis and
simulation of an adaptive image coding system using the
LMS algorithm,” in Proc. 1982 IEEE Int. Conf. Acoust.,
Speech Signal Processing, Paris, France, May 1982.
[8]. W. K. Pratt, Digital Image Processing. New York:
Wiley, 1978.
[9]. J. E. Modestino, and D. G. Daut, “Source-channel
coding of image,” IEEE Trans. Commun., vol. COM-27, pp.
1644-1659, Nov1979.

Image Compression using DPCM with LMS Algorithm

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Image Compression using DPCM with LMS Algorithm