Comprehensive Study of the Work Done In Image Processing and Compression Techniques for Redundancy

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 03 Issue: 01 | Jan-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 855
COMPREHENSIVE STUDY OF THE WORK DONE IN IMAGE PROCESSING
AND COMPRESSION TECHNIQUES FOR REDUNDANCY
Suman kumari 1, Sona khanna 2 , Taqdir 3
12Student M.tech (CSE), Computer Science, Guru Nanak Dev University RC, Gurdaspur, India
3Assistant Professor, Computer Science, Guru Nanak Dev University RC, Gurdaspur, India
Abstract :Image processing is one of the latest
topics and is used in almost every field of the
study. Image processing is used in order to
enhance, smoothening, filtering etc. the image.
There are number of techniques which are
suggested in order to enhance and smoothening
the image. When images merged together than
pixels may overlap with each other. This
overlapping of image will cause redundancy
within the image. In our paper we will analyze
this redundancy problem. We will also try to
rectify the problem. In order to build a base we
have analyze large number of papers. Some of
the papers have described the problem of
redundancy. In this paper review of those papers
has been conducted. Keyword: Image, image
compression,
redundancy, lossy compression, lossless
compression.
1. Introduction
Today even the naïve user is using the computer.
Naïve users do not now much command
associated with the computers. So in order to
make them understand the computer, Graphical
User Interface is required. This interface will be
provided with the help of applications of image
processing. So main application of image
processing is in the field of interface designing.
The interface is important as it will lead to the
success or failure of the system. When interface
is designed we may required in order merging
multiple images together. When this happens
pixels may overlap with each other. This
overlapping of pixel will cause redundancy in the
images. Redundancy within the image will cause
number of problems. The redundancy will cause
extra space requirements. Hence when
redundancy is present than extra cost will be
encountered. The redundancy can also be

present due to compression technique which is
used. The compression method can be lossy or
lossless in nature. The redundancy problem is
common in lossless compression.
2. Image compression techniques
There are number of image compression
techniques which are available to be used. Each
compression technique which is used is complex
in nature. Compression techniques are divided
into following categories.
1) Lossy Compression
2) Lossless Compression
1) Lossy compression techniques
In lossy compression, reconstructed image, after
compression is not identical to the original image
but close to it. [1] These techniques are applied
to those data where loss is tolerable. Following
are lossy compression techniques:-
a) Transformation Coding
In this technique Discrete Fourier
transformation is used. In this technique the
pixel in original image is changed to frequency
domain. The overall energy of the entire pixels
are concentrated on the few necessary pixels.
Only some significant pixels are selected and rest
of the pixels is rejected.
b) Vector quantization
In this case dictionary of code vectors are
prepared. Code vector is the block of pixel
values. The given image is then divided into
parts. This is known as image vector. The image
vector is than compared with the code vector in
order to determine the code for the given image.
c) Fractal Coding
The idea behind this encoding is to divide the
image into parts. The image will be divided into
parts on the basis of colors, edge detection etc.
The technique is useful in case image contain
redundancy.
d) Block Truncation Coding
The image in this case is divided into block of
non overlapping pixels. For each block threshold
and reconstruction values are determined. The
values of the block will then be compared against
the threshold values. If the pixel values are
greater than the threshold values than the pixel
values will be rejected. This technique is not
useful the redundancy is presented within the
images.
2) Lossless Compression Techniques
In lossless compression, reconstructed image,
after compression is identical to the original
image. [1] It is applied to those data in which we

need accuracy such executable codes. Following
are lossless compression techniques:-
a) Run Length Encoding
This is very simple form of encoding. In this case
encoding the larger set of string is replaced with
the smaller set of code. The Run Length Encoding
is useful in a system where large number pixels
repeat itself. In order to describe this system we
will take following example of string
1122223344444444444443333333333333222
222222255555555555
{1,2}, {2,4},{3,2},{4,13},{3,13},{2,10},{5,11}
In this type of encoding the frequency of each
digit is included within the braces along with the
digit itself. Within the braces first variable
represent the symbol which are repeated and
second variable represent the length of symbol.
b) Huffman Encoding
Huffman Encoding is used to encode the given
image into set of codes. The codes can be
represented in the form a tree. The reverse
approach is followed in order to formulate a
code. The code is generally represented in the
form of binary string.
Fig 1. Example of Huffman encoding
The last two bits are extracted and then added
together to achieve the value of the root node.
This process continues until the root node is
reached or tree terminated.
c) LZW Coding
LZW (Lempel- Ziv – Welch) is a dictionary based
coding. [2] Dictionary based coding can be static
or dynamic. The static dictionary coding
describes dictionary as a fixed during the
encoding and decoding processes. The dynamic
dictionary coding describes the dictionary as
updated on fly. LZW is widely used in computer
industry and is implemented as compress
command on UNIX.

Fig 2. Example of LZW coding
3. Related Work
There are number of papers which described the
problem of redundancy in images. In order to
build the base we analyze number of such
papers. Some of the papers which we have
studied will be described in this section.
Redundancy will make certain portion of the
image much brighter than the other portion of
the images. [3] This paper considers the
compression technique for jpeg images. The jpeg
images are common extension for the images
which are being transferred. The transferred
images will be compressed so that image should
not take much space over the transmitted
medium. The transmission media will charge
expenses if the data transferred are large. So
compression is required. Discrete cosine
transform is used in this case to compress the
image. If image is compressed properly than less
bits per image is required to represent the
image. Hence the mechanism of image
compression will help in decreasing the cost
associated with the image storage.[4] In addition
to the redundancy image processing also
contains the problem of noise. This considered
paper considers the impact of noise on the
image. The impact of noise will cause distorted
image. Image denoising via sparse and
redundant representations over learned
dictionaries will be considered in this case. The
advantages of the redundancy will be shown. In
other word the positive side of the redundancy
will be considered in this case. [4] There are
number of types of redundancy which are
present within the image. The pixels will have
large spaces in between the pixels. This is known
as inter pixel distance. In order to reduce the
distance compression techniques are followed.
In order to efficiently compress the images
compression techniques are used. Compression
technique which is suggested in this paper
includes lossless and lossy compression. The
redundancies which are considered are
interpixel, coding and Psycho visual.[5]The
image compression will be considered in this
case. Image compression is required so that the
space requirements can be reduced. The image
compression will be required to reduce the
redundancy. The type of redundancy which is
considered in this case will include Psycho
visual. This redundancy indicates sensitivity to
different images by human eye. So some

unnecessary information from the image can be
rejected. [6] Image compression techniques are
considered. The image compression will be used
so that relatively less pixels should be used in
order to represents the image. Sometimes image
does not contain any relevant data. In that case
that irrelevant information has to be eliminated.
This is accomplished with the help of
compression techniques. [7]The concept of
medical images is considered in this case. The
MRI is a form of images which are used in the
area of medical field. Various types of
redundancies are present within the images.
These redundancies are eliminated by the use of
compression techniques. In this paper the area of
concern is medical images. [8]The study of
various image compression techniques are
considered in this case. Principal Component
Analysis technique is considered in this case.
Image f(x,y) is fed into the encoder, which
creates a set of symbols form the input data and
uses them to represent the image. If we let n1
and n2 denote the number of information
carrying units( usually bits ) in the original and
encoded images respectively, the compression
that is achieved can be quantified numerically
via the compression ratio. The main area of
concern is Huffman coding, LZW coding etc. PCA
technique suggested in this paper is based upon
two factors data reduction and interpretation.
The main focus of all the papers studied is data
compression and reducing the redundancy
present within the image. The techniques which
are suggested within the papers are very
complex and time consuming.
4. Conclusion and Future Work
The papers we have analyzed study the
compression techniques. The compression
techniques which are specified are lossy or
lossless in nature. All the suggested techniques
use complex mechanisms in order to reduce the
redundancies. In the proposed work we will use
relatively simple mechanism to reduce the
redundancy from the given image. The proposed
method will use the buffer in order to store the
threshold values which can be compared against
the newly generated pixels to reject them if they
are repeated.
REFERENCES
[1] S. M. C. .. Athira B. Kaimal, "Image
Compression Techniques: A Survey,"
International Journal of Engineering
Inventions, vol. 2, no. 4, pp. PP: 26-28,
February 2013.

[2] R. R. Sindhu M, "A study of various image
compression," International journal of recent
trends in engineering, vol. 2, no. 4, 2009.
[3] W. M. A. E.-d. a. W. A. A.M.Raid, "Jpeg Image
Compression Using Discrete Cosine,"
International Journal of Computer Science &
Engineering Surve, vol. 5, no. 2, April 2014.
[4] M. a. M.aharon, "Image denosing via sparse
and redundant representations over learned
dictionaries," IEEE transaction image
processing, vol. 15, no. 12, 2006.
[5] N. Kaur, "A Review of Image Compression
Using Pixel," International Journal of
Application or Innovation in Engineering &
Management, vol. 2, no. 1, 2013.
[6] D. B. V. D. Y. R. P. B. S. T. Rohini Salunke, "The
State of the Art in Image Compression,"
International Journal of Advanced Research in
Computer and Communication Engineering,
vol. 4, no. 2, 2015.
[7] P. P. Suneel Kumar1, "Performance
Evaluation of K-RLE," International Journal of
Advanced Research in Computer and
Communication Engineering, vol. 4, no. 6,
2015.
[8] S. Stolevski, "Hybrid PCA Algorithm for Image
Compression," in 18th Telecommunications
forum TELFOR 2010, Serbia, Belgrade, 2010.

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