2 ijaems dec-2015-5-comprehensive review of huffman encoding technique for image compression

International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-1, Issue-9, Dec- 2015]
Infogain Publication (Infogainpublication.com) ISSN : 2454-1311
www.ijaems.com Page | 8
Comprehensive Review of Huffman Encoding
Technique for Image Compression
Sona Khanna1
,Suman Kumari2
,Tadir3
1,2
Student M.Tech (CSE), Computer Science, Guru Nanak Dev University RC, Gurdaspur, India
3
Assistant Professor, Computer Science, Guru Nanak Dev University RC, Gurdaspur, India
Abstract—The image processing is used in the every field
of life. It is growing field and is used by large number of
users. The image processing is used in order to remove the
problems present within the image. There are number of
techniques which are suggested in order to improve the
image. For this purpose image enhancement is commonly
used. The space requirements associated with the image is
also very important factor. The main aim of the various
techniques of image processing is to decrease the space
requirements of the image. The space requirements will be
minimized by the use of compression techniques.
Compression techniques are lossy and lossless in nature.
This paper will conduct a comprehensive survey of the
lossless compression Huffman coding in detail.
Keywords— Huffman coding, Image compression, Image
processing.
I. INTRODUCTION
The Huffman Coding is a compression technique which is
lossless in nature. In lossless compression the pixel
information is not lost. The Huffman coding uses tree like
structure. The tree like structure will help in generating
unique code which can be easily decodable. The advantages
of using this technique are that it will produce minimum bit
rate for every codeword generated. The Huffman Coding
will be best described by the help of following example.
Fig. 1:Demonstration of Huffman coding
The Fig 1 shows the example in which every character of
the string is divided into parts and then separately coded.
The example takes the value of L=6 with the probability of
each message possibility will be noted at each node.
Fig. 2: Demonstration of Huffman coding
In the first step the message with the lowest probabilities
are selected. They are combined together to generate new
node with combined probability. ‘0’ will be assigned to one
of the possibility and ‘1’ will be assigned to other
possibility. The process continues until we are left with the
single node with probability ‘1’. In order to generate the
codeword we begin from the last node having possibility ‘1’
and move to the desired node collecting sequences of ‘0’
and ‘1’. As shown in the Fig 2 the message a4 has
Codeword ‘1110’. The Huffman coding generally produces
the least average bit rate as compared to other methods. The
existing approach follows iterative approach. Also existing
scheme follows Binary search mechanism. The efficiency
of the technique can be increased if Heaps along with
recursive approach is followed. The analysis of the existing
papers highlighted the advantages and disadvantages of the
existing approach.

II. RELATED WORK
The study has been conducted in the area of efficient
encoding schemes. The coding schemes which are used in
the paper has lower average bit rate. The existing system
uses iterative approach. Also bottom up approach is
followed in the existing system. The various papers which
we have considered are described in this section. [1] The
paper indicates that compression techniques can be used in
order to reduce the space requirements associated with the
image. The correlation between the pixels is used in order to
determine the amount of space which can be released. The
calculations which are performed in the suggested technique
is complex in nature. [2] The massive number of medical
images produced by fluoroscopic and other conventional
diagnostic imaging devices demand a considerable amount
of space for data storage. [2]This paper proposes an
effective method for lossless compression of fluoroscopic
images. [2]The main contribution in this paper is the
extraction of the regions of interest (ROI) in fluoroscopic
images using appropriate shapes. [2]The extracted ROI is
then effectively compressed using customized correlation
and the combination of Run Length and Huffman coding, to
increase compression ratio. [2]The experimental results
achieved show that the proposed method is able to improve
the compression ratio by 400 % as compared to that of
traditional methods. [3] Lossy JPEG compression
techniques are followed in this case. In this case pixel
values are not preserved. The blocks of pixel are prepared
and record of those pixels which are most commonly used
are retained and values of less significant pixels are
removed. The technique used in this case is Fourier
Transformation which will require large number of
calculations. [4] The JPEG compression is followed in this
case. The technique suggested in this case is discrete cosine
transformation. The technique is complicated and also
produce high bit rate for the given section of the image. [5]
the image processing technique is implemented for medical
image processing. In medical generally image processing is
used for MRI, X-Rays etc. The need for an efficient
technique for compression of Images ever increasing
because the raw images need large amounts of disk space
seems to be a big disadvantage during transmission &
storage. Even though there are so many compression
technique already present a better technique which is faster,
memory efficient and simple surely suits the requirements
of the user. In this paper we proposed the Lossless method
of image compression and decompression using a simple
coding technique called Huffman coding. This technique is
simple in implementation and utilizes less memory. A
software algorithm has been developed and implemented to
compress and decompress the given image using Huffman
coding techniques in a MATLAB platform. [6]Principal
Component Analysis will be used in this case. This
technique is used in order to select those components which
are required within the image. The primary objective of the
above said technique is to remove the redundancy from the
image. This technique is very efficient however complex to
use. The higher bit rate is produced using this technique. [7]
For courses in Image Processing and Computer Vision.
Completely self-contained-and heavily illustrated-this
introduction to basic concepts and methodologies for digital
image processing is written at a level that truly is suitable
for seniors and first-year graduate students in almost any
technical discipline.[7] The leading textbook in its field for
more than twenty years, it continues its cutting-edge focus
on contemporary developments in all mainstream areas of
image processing-e.g.,[7] image fundamentals, image
enhancement in the spatial and frequency domains,
restoration, color image processing, wavelets, image
compression, morphology, segmentation, image description,
and the fundamentals of object recognition. [7]It focuses on
material that is fundamental and has a broad scope of
application. [8] the paper suggested the technique of
Huffman Encoding using the binary search technique. The
Huffman Coding is a compression technique which is
lossless in nature. In lossless compression the pixel
information is not lost. The Huffman coding uses tree like
structure. The tree like structure will help in generating
unique code which can be easily decodable. The advantages
of using this technique are that it will produce minimum bit
rate for every codeword generated.
From the studied papers it is clear that in the existing
system iterative approach is followed. Also in case of
Huffman coding binary search techniques are used.
III. CONCLUSION
The review is conducted in order to determine which
method is best to produce least average bit rate. The
technique which comes out is Huffman coding. The
technique which is used in this case is iterative. Also binary
search is used rather than heap sort. In order to increase the
performance we propose Recursive Huffman coding using
Heap sort. The technique will produce least average bit rate
and hence is less expensive.
REFERENCES
[1] N. Kaur, “A Review of Image Compression Using
Pixel Correlation & Image Decomposition with,”

vol. 2, no. 1, pp. 182–186, 2013.
[2] A. S. Arif, S. Mansor, R. Logeswaran, and H. A.
Karim, “Auto-shape Lossless Compression of Pharynx
and Esophagus Fluoroscopic Images,” J. Med. Syst.,
vol. 39, no. 2, pp. 1–7, 2015.
[3] W. M. Abd-elhafiez and W. Gharibi, “Color I mage C
ompression A lgorithm B ased on the DCT B locks,”
vol. 9, no. 4, pp. 323–328, 2012.
[4] R. a.M, K. W.M, E. M. a, and W. Ahmed, “Jpeg
Image Compression Using Discrete Cosine Transform
- A Survey,” Int. J. Comput. Sci. Eng. Surv., vol. 5,
no. 2, pp. 39–47, 2014.
[5] G. Badshah, S. Liew, J. M. Zain, S. I. Hisham, and A.
Zehra, “Importance of Watermark Lossless
Compression in Digital Medical Image
Watermarking,” vol. 4, no. 3, pp. 75–79, 2015.
[6] S. Stolevski, “Hybrid PCA Algorithm for Image
Compression,” pp. 685–688, 2010.
[7] R. C. . Gonzalez and R. E. Woods, “Digital Image
Processing,” p. 976, 2010.
[8] D. G. Sullivan and D. Ph, “Binary Trees and Huffman
Encoding Binary Search Trees Fall 2012,” 2012.

2 ijaems dec-2015-5-comprehensive review of huffman encoding technique for image compression

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