Performance analysis of high resolution images using interpolation techniques in multimedia communication system

Signal & Image Processing : An International Journal (SIPIJ) Vol.5, No.2, April 2014
DOI : 10.5121/sipij.2014.5204 39
PERFORMANCE ANALYSIS OF HIGH
RESOLUTION IMAGES USING INTERPOLATION
TECHNIQUES IN MULTIMEDIA
COMMUNICATION SYSTEM
Apurva Sinha1
, Mukesh kumar2
, A.K. Jaiswal3
, Rohini Saxena4
Department of Electronics and Communication Engineering,
SHIATS- Allahabad, UP.-India
ABSTRACT
This paper presents various types of interpolation techniques to obtain a high quality image The difference
between the proposed algorithm and conventional algorithms (in estimation of missing pixel value) is that
if standard deviation of image is used to calculate pixel value rather than the value of nearmost neighbor,
the image gives the better result. The proposed method demonstrated higher performances in terms of
PSNR and SSIM when compared to the conventional interpolation algorithms mentioned.
KEYWORDS
Interpolation, Bicubic, Bilinear, Nearest Neighbor, PSNR, SSIM
1. INTRODUCTION
Low bandwidth and limited channels are one of the most challenging issues that every nation in
this world is facing. Since signals and data to be sent via a channel are available in huge amount
and limited amount of spectrum is allotted to every nation, hence signal needs to be compressed
at the transmitter and expand at receiver in order to send multiple data. This compression and
expansion leads to distortion of signals and sometimes even leads to false reconstruction at the
receiver side. Thus the performance of system decreases and this also hinders in smooth
operation of the system. If the channel is wireless the situation even becomes worse. Different
amount of noise gets entered into the channel and try to deteriorate the signal. Sometimes the
signal is even lost while traversing. If a digital image is sent via a wireless channel it has to be
compressed at the transmitter because of limited channels. At receiver it may get distorted due to
the presence of noise and during image expansion it might get deviated from its original form.
Some features of images are hardly detectable by an eye so they should be often transformed
before display. Image enhancement is a digital processing method which does its best to improve
image vision and makes the image adapt to be processed by computer. It enhances some
information inside the image selectively and restrains the other ones. To reconstruct or expand

40
the image to get the original form, Upsampling or Interpolation of images is done. The paper is
divided into six sections, section 2 gives a brief introduction about Interpolation Algorithms ,
section 3 covers some methods related to Interpolation , Section 4 discusses about our proposed
method, section 5 gives us the result and discussion, and followed by the conclusion in section 6.
2. INTERPOLATION
Interpolation is the process of enlargement of images by creating new pixel values and filling the
values appropriately by some algorithms[1]. Zooming requires new pixel values. Interpolation
adds pixel values to this newly generated pixel according to the value of mean of near most
pixels.
3. RELATED WORKS
Some conventional methods has been described below showing how Bicubic Interpoltion is more
efficient.
3.1 Nearest Neighbor
One of the simplest interpolation algorithms is Nearest-Neighbor interpolation. In order to
upsample or zoom an image Nearest Neighbor provides easiest way [2].Image enlargement
requires two steps:- First is creation of new pixel locations and second is assignment of pixel
values to those locations. This can be done by treating image as a matrix and creating new rows
and columns by padding it with matrix having double the size of original image matrix and
having only zero value so that every alternate rows or columns of resultant matrix contains zero
as its pixel value. Next step is to assign the pixel value of the near most neighbor to the newly
generated pixel. That is why this method of grey level assignment is called Nearest Neighbor
Interpolation. The flowchart is shown below

41
3.1.1 Flow Chart
3.2 Bilinear Interpolation
The biggest drawback of nearest neighbor interpolation is that it cannot be used in high resolution
zooming because it causes stair case edges. An interpolation technique that reduces the visual
distortion caused by the fractional zoom calculation is the bilinear interpolation algorithm [3]. It
is performed in one direction first (row wise) then again in other direction (column wise) . It uses
four nearest neighbor of pixel whose value is to be determined. An image is selected and it is
converted into matrix form. Another image of size 2m*2n is taken which contain zero elements.
This matrix is padded with the matrix of image so that the resultan matrix contain zero elements
in every alternate row and column. The weighted average of four pixels are calculated and the
result is put into the newly generated pixel.The final pixel value v(x,y) of x row and y column is
calculated as follows:-
Start
Select a RGB Image of size m*n
Divide it into Red, Green and Blue Planes
Take a matrix of size 2m*2n containing only zero
elements .
Copy pixel value of jth column and ith row
to j+1th column and i+1th row
.
All Elements
Covered?
Repeat it for Green and Blue planes and concatenate
the three planes to get an RGB Image
Stop
Pad the zero matrix to original matrix so that every alternate
rows and column contain zero elements

42
v (x , y ) = a – b * v(x,y-1) + c – d * v (x , y+1)
Where a = (( x+1) - x ) / ( x + 1 )
b = (x – 1)
c = (x - (x – 1)) / (x + 1)
d = (x – 1)
3.2.1 Flow chart
3.3 Bicubic Interpolation
High order interpolation schemes take more pixels into account. Second order interpolation is
called as Cubic Interpolation as it uses a neighborhood of 16 pixels [4]. When speed is not an
issue, Bicubic Interpolation is often chosen over Bilinear Interpolation or Nearest Neighbor in
image enhancement. As compare to bilinear interpolation, which takes only 4 pixels (2x2) into
Start
Take a matrix of size 2m*2n containing only
zero elements .
Take the mean of j-1th and j+1th column and put the
result in jth column. Repeat the process for rows
also.
All Elements
Covered?
Repeat it for Green and Blue planes and concatenate
the three planes to get an RGB Image
Stop
Pad the zero matrix to original matrix so that
every alternate rows and column contain zero
elements

43
account, Bicubic Interpolation considers 16 pixels (4x4). Images resample with bicubic
interpolation are smoother and b;ur is not formed even when image is interpolated many times. It
fits two polynomials to the 16 pixels of the transformed original matrix and the centre of the new
image pixel .This technique is very effective and produces images that are very close to the
original image .
3.3.1 Flow Chart
Start
Take a matrix of size 2m*2n containing only
zero elements .
Take near by 16 pixels, find their mean and put the
result in jth column and ith row.
All Elements
Covered?
Repeat it for Green and Blue planes and
concatenate the three planes to get an RGB
Image
Stop
Pad the zero matrix to original matrix so that every alternate
rows and column contain zero elements

44
4. PROPOSED METHOD TO CALCULATE PIXEL VALUE
Many advantages of Bicubic Interpolation technique make it suitable for its use in image
processing applications. But it increases computational complexity and hence it is suitable only in
3D Graphics and Medical Imaging. For general purpose, Bilinear Interpolation is used as it is
time saving method [5]. When noise is added in the image bilinear interpolation cannot remove it
effectively. The proposed methodology, described below,will reduce noise more effectively than
the bilinear interpolation [6].
4.1 Algorithm :-
The steps are as follows :-
1. Take the Color Image (RGB) ‘Lena.jpg’ of size 512*512 .
2. Add Salt and Pepper noise ( or any other noise like Gaussian, Speckle etc ) in it.
3. Decimate or Reduce the size of image to 256*256 pixels to convert it into a low
resolution image.
4. Transmit the image at transmitter.
5. At receiver, create a matrix of size 512 *512 containing only zero elements and pad it
with reduced matrix so that every alternate rows and columns of zoomed matrix is filled
with only zero elements.
6. Take average of j+1th and j-1th pixel value column and put the result in jth column.
Repeat the same process for rows also.
7. Take each 3*3 matrix within the enhanced matrix, find its Mean and put the result in the
centre most point of the matrix. Repeat the process till all points are covered.
The above process [8], no doubt, reduces noise density but when the image is interpolated many
times, it introduces blur in the image [7]. Thus in order to avoid blur, image is interpolated
keeping in mind the Standard Deviation of the image. Standard Deviation depicts how much
variation is there from the average value . A low standard deviation indicates that the data points
are very close to the mean (also called expected value); a high standard deviation indicates that
the data points are spread out over a large range of values. When a new pixel is created, it has to
be assigned with the pixel value by choosing the values from the near most neighbor’s pixel
value. If mean of neighboring pixels are taken and to this mean if the standard deviation of image
is added, than pixel value will be distributed more smoothly and uniformly within the image
which is interpolated.
The steps taken to reduce the blur are as follows:-
4.2 Algorithm
1. Take the Color Image (RGB) ‘Lena.jpg’ of size 512*512 .
2. Decimate the image by a decimation factor of 4 and reduce the size of image to 128*128
pixels to convert it into a low resolution image.
3. Transmit the image at transmitter.
4. At receiver, generate a random variable , find its Probability Distribution Function and
finally calculate the Standard Deviation of the image.

Signal & Image Processing : An Int
5. Take each 2*2 matrix within the reduced matrix, find its mean. To this mean, add the
value of Standard Deviation and put the result in the centre most point of the matrix.
5. RESULT AND DISCUS
The resultant images obtained due to interpolation via Bilinear Algorithm and Proposed
Methodology is given as follows :
a. Bilinear Interpolated Image
Fig 1 Comparison of images obtained by
The resultant image obtained due to Proposed Methodology has less no
Bilinear Interpolated Image. The table of comparison for PSNR of Bilinear Interpolation and
interpolation method by Proposed Methodology is drawn below for Decimation Factor of 2:
SN NO Noise Density
1
2 0.05
3 0.1
4 0.15
5 0.2
Table 1 Comparison of PSNR values of Bilinear Interpolation and Interpolation by Proposed
The table indicates that as the value of noise decrease the value of PSNR in both methods
decreases. The decrease in PSNR of Proposed Method is less tha
method.
Take each 2*2 matrix within the reduced matrix, find its mean. To this mean, add the
ISCUSSION
Methodology is given as follows :-
ed Image b. Image obtained by Proposed Method
Fig 1 Comparison of images obtained by a.) Bilinear Interpolation b.) Proposed Method
The resultant image obtained due to Proposed Methodology has less no is as compared to
The table of comparison for PSNR of Bilinear Interpolation and
osed Methodology is drawn below for Decimation Factor of 2:
Noise Density PSNR of Bilinear PSNR of
Proposed Method
0 31.1307 29.8789
0.05 27.409 27.4432
0.1 25.5191 26.2477
0.15 24.9956 25.4834
0.2 24.4309 24.974
Methodology
decreases. The decrease in PSNR of Proposed Method is less than that of Bilinear Interpolation
ernational Journal (SIPIJ) Vol.5, No.2, April 2014
45
Take each 2*2 matrix within the reduced matrix, find its mean. To this mean, add the
b. Image obtained by Proposed Method
a.) Bilinear Interpolation b.) Proposed Method
is as compared to
The table of comparison for PSNR of Bilinear Interpolation and
osed Methodology is drawn below for Decimation Factor of 2:-
n that of Bilinear Interpolation

46
PSNR of an image computes Peak Signal to Noise Ratio which determines how much is the ratio
of signal power to that of noise Power [9]. It is given as
PSNR = 20∗ ‫܏ܗܔ‬૚૙
૛૞૞
ࡾࡹࡿࡱ
where the value 255 is maximum possible value that can be attained by the image signal. Mean
square error (MSE) is defined as
MSE =
૚
‫ܖܕ‬
∑ ∑ [۷ሺܑ, ‫ܒ‬ሻ − ۹ሺܑ, ‫ܒ‬ሻ]૛‫ିܖ‬૚
‫ܒ‬ୀ૙
‫ିܕ‬૚
ܑୀ૙
RMSE=√ࡹࡿࡱ
The Structure Similarity Index Measurement compares original image with reconstructed images
based on its luminence, intensity and saturation. It depicts how closely reconstructed image is
similar to original image when seen by human eyes.
The graph of SSIM for both interpolation algorithms is shown below:-
Fig 3 showing different values of SSIM w.r.t noise for Bilinear Interpolation and Algorithm by
Proposed Method.
The above graph indicates that as the value of noise density increases SSIM value for Bilinear
Interpolation decreases at a greater extent as compared to image obtained by Proposed
Methodology.
In above process, noise is reduced but introduces blur in the image. So, again a method is
proposed to reduce the blur by using standard deviation of the image. The resultant images are
shown as follows:

47
Fig 3 shows the resultant images obtained by a.) Nearest Neighbor b.) Bilinear Algorithm
c.) Proposed Method
The result shows stair case edges in Nearest Neighbor Interpolation, blur in Bilinear Interpolation
and least blur in Proposed methodology.
The graph for PSNR for various interpolation methods is shown below:-
Fig. 4 Graph of PSNR for different Interpolation Algorithms
The above graph shows that the PSNR of image interpolated by proposed methodology gives the
maximum value of 33.497 dB followed by Bilinear Interpolation (32.9748 dB) and Nearest
Neighbor Interpolation (31.7644 dB).The output image due to Proposed Methodology can also be
compared with conventional interpolation techniques using Structure Similarity Index
Measurement. The graph obtained is as follows:

48
Fig 5 Graph of SSIM for different Interpolation Algorithm
The results obtained above indicate that both the images obtained by Proposed Methodology
provide better results as compared to conventional interpolation algorithms.
6. CONCLUSION
Nearest Neighbor is the fastest and simplest method of interpolation, but leads to stair case
Edges. Hence it is not used frequently. Bicubic Interpolation give good results but causes
computational complexity. Hence it is used in 3D Graphics. Bilinear Interpolation is
comparatively better than Nearest Neighbor but causes blur of images [10]. It can be concluded
that images obtained by Proposed Methodology gives better result as compared to conventional
interpolation algorithms.
REFERENCES
[1] Rafael C. Gonzalez and Richard E. Woods,(2002), “Digital Image Processing” , Second Edition,
Pearson Publishing..
[2] Cao Hanqiang and Oliver Hukundo,(2012), “Nearest Neighbor Interpolation”, IJACSA, Vol-11 , No-
4..
[3] Kiyoshi Arai, Tsuneya Kurihara, Ken-ichi Anjyo, (1996), “Bilinear interpolation for facial expression
and metamorphosis in real-time animation”, The Visual Computer, Volume 12, Issue 3, pp 105-116
[4] Robert G Keys,(1981),” Cubic Convolution Interpolation for Digital Image Processing “,Vol. ASSP-
29, No. 6, December.
[5] Alhan Anwer Yunis , (2013),”Comparison Among Some Image Zooming Methods”, College of Basic
Education Researchers Journal Vol. (12), No.(3).
[6] Donald E Troxel and J. Anthonny Parker,(1983),”Comparison of Interpolating Methods for Image
Resampling”, IEEE, Vol-1, No-2, March .
[7] William K. Pratt, (2001),”Digital Image Processing”, Third Edition , Johny Wiley and Sons.
[8] Rafael C. Gonzalez,(2009),“Digital Image Processing Using Matlab”,Second Edition, Gatesmark
Publishing.
[9] S. Sridhar, (2011),”Digital Image Processing”, Oxford University Press.

49
[10] Chidananda Murthy M V,Vanishree Yallapurmath,()”Design and Implementation of Interpolation
Algorithms for Image Super Resolution “, 8th IEEE ,IET International Symposium on
Communication system, Networks and Digital Signal Processing.
AUTHORS
Apurva Sinha received her B.Tech Degree from department of Electronics And
Communication Engineering from Mahatma Jyotiba Phule Rohilkhand University
Engineering and Technology Bareilly, Uttar Pradesh in 2012.She is pursuing M.Tech from
the Department of Electronics and Communication SHIATS, Allahabad. Her main
research interest includes Communication System Engineering, Image Processing Systems
etc.
Mukesh Kumar is working as a Asst. Prof. in the Department of Electronics &
Communication Engineering in SHIATS, Allahabad. He received his M.Tech. Degree
in Advanced Communication System Engineering from SHIATS, Allahabad in 2010.
His research is focused on Signal processing and Microwave Engineering.
A.K. Jaiswal is Prof. and Head of ECE Dept at SHIATS-Allahabad.He Obtained M.Sc. in
Tech. Electronic & Radio Engg. from Allahabad University in 1967.He guided various
projects & research at undergraduate & postgraduate level. He has more than 35years
Industrial, research & Teaching experience and actively involved in research and
publications. His area of interest includes Optical Networks and satellite communication.
Rohini Saxena is working as a Asst. Prof. in the Department of Electronics &
Communication Engineering in SHIATS, Allahabad. She received her M.Tech. Degree
in Advanced Communication System Engineering from SHIATS, Allahabad in 2009.
Her research is focused on Digital Communication, Microwave Engineering, Wireless
Sensor Network, Computer Networks and Mobile Communication.

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