Lossless Image Compression Techniques Comparative Study

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072
© 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 1
Lossless Image Compression Techniques
Comparative Study
Walaa Z. Wahba1, Ashraf Y. A. Maghari 2
1M.Sc student, Faculty of Information Technology, Islamic university of Gaza, Gaza, Palestine
2Assistant professor, Faculty of Information Technology, Islamic university of Gaza, Gaza, Palestine
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract- In image compression, we can reduce the
quantity of pixels used in image representation without
excessively change image Visualization. Reducing
image size enhance images sharing, transmitting and
storing. This paper examines the performance of a set
of lossless data compression algorithms which are RLE,
Delta encoding and Huffman techniques on binary
image, grey level images and RGB images.
The selected algorithms are implemented and
evaluated on different aspects like: compression ratio,
saving storage percentage and compression time. A set
of defined images are used as test bed. The
performance of different algorithms are measured on
the basis of different parameter and tabulated.
The results showed that delta algorithm is the best in
case of compression ratio and saving storage
percentage, while Huffman encoding is the best
technique when evaluated by compression time.
Key Words: compression; image; Delta; RLE;
Huffman; coding; algorithm
1. INTRODUCTION
WITH the invention of recent smart computing devices,
generating, transmitting and sharing of digital images have
excessively been increased. The more the small electronic
devices are incorporating cameras and providing the user
with technologies to share the captured images directly to
the Internet, the more the storage devices are grasping the
necessity of effectual storing of huge amount of image data
[1].
Transmission of raw images over different types of
networks required extra demand of bandwidth because
images data contains more information than simple text or
document files [1]. Therefore image size need to reduce
before they are either stored or transmitted. Diverse
studies and researches have been conducted regarding
how an image data can be best compressed apart from
sacrificing the quality of the image.
Image compression methods can be classified in several
ways. One of the most important criteria of classification is
whether the compression algorithms remove some part of
data which cannot be recovered during decompression.
The algorithm which removes some part of data is called
lossy data compression. And the algorithm that achieve
the same what we compressed after decompression is
called lossless data compression [2]. The lossy data
compression algorithm is usually use when a perfect
consistency with the original data is not necessary after
decompression. Example of lossy data compression is
compression of video or picture data. Lossless data
compression is used in text file, database tables and in
medical image because law of regulations. Various lossless
data compression algorithm have been proposed and
used. Some of main technique are Huffman Coding, Run
Length Encoding, Delta encoding, Arithmetic Encoding and
Dictionary Based Encoding. In this paper we examine
Huffman Coding and Arithmetic Encoding and give
compression between them according to their
performances.
This paper examines the performance of the Run Length
Encoding Algorithm (RLE), Huffman Encoding Algorithm
and delta Algorithm. Performance of above listed
algorithms for compressing images is evaluated and
compared.
The remainder of this paper is organized as follows:
Section II describes the background concepts of image
compression and related work. Section III describes
statement of the problem. Section IV describes used
methodology. Section V describes details experiment
results and discussion. Section VI describes conclusion and
future work.
2. Background
2.1 IMAGE COMPRESSION
Image compression is the process intended to yield a
compact representation of an image, thereby reducing the
image storage and transmission requirements by reducing
the amount of information required to represent a digital

image. Every image will have redundant data. Redundancy
means the duplication of data in the image. Either it may
be repeating pixel across the image or pattern, which is
repeated more frequently in the image. The image
compression occurs by taking benefit of redundant
information of in the image. Reduction of redundancy
provides helps to achieve a saving of storage space of an
image. Image compression is achieved when one or more
of these redundancies are reduced or eliminated. In image
compression, three basic data redundancies can be
identified and exploited. Compression is achieved by the
removal of one or more of the three basic data
redundancies [3].
2.1.1 Inter Pixel Redundancy
“In image neighboring pixels are not statistically
independent. It is due to the correlation between the
neighboring pixels of an image. This type of redundancy is
called Inter-pixel redundancy. This type of redundancy is
sometime also called spatial redundancy. This redundancy
can be explored in several ways, one of which is by
predicting a pixel value based on the values of its
neighboring pixels. In order to do so, the original 2-D array
of pixels is usually mapped into a different format, e.g., an
array of differences between adjacent pixels. If the original
image pixels can be reconstructed from the transformed
data set the mapping is said to be reversible” [4].
2.1.2 Coding Redundancy
“Consists in using variable length code words selected as
to match the statistics of the original source, in this case,
the image itself or a processed version of its pixel values.
This type of coding is always reversible and usually
implemented using lookup tables (LUTs). Examples of
image coding schemes that explore coding redundancy are
the Huffman codes and the arithmetic coding
technique”[3].
2.1.3 Psycho Visual Redundancy
Many experiments on the psycho physical aspects of
human vision have proven that the human eye does not
respond with equal sensitivity to all incoming visual
information; some pieces of information are more
important than others. Most of the image coding
algorithms in use today exploit this type of redundancy,
such as the Discrete Cosine Transform (DCT) based
algorithm at the heart of the JPEG encoding standard [3].
2.2 Types of Compression:
Compression can be of two types: Lossless Compression,
Lossy Compression.
2.2.1 Lossless Compression:
In the process compression if no data is lost and the exact
replica of the original image can be retrieved by
decompress the compressed image then the compression
is of lossless compression type. Text compression is
generally of lossless type. Lossless compression technique
can be broadly categorized in to two classes [6]:
 Entropy Based Encoding: In this compression process
the algorithm first counts the frequency of occurrence
of each pixel in the image. Then the compression
technique replaces the pixels with the algorithm
generated pixel. These generated pixels are fixed for a
certain pixel of the original image; and doesn’t depend
on the content of the image. The length of the
generated pixels is variable and it varies on the
frequency of the certain pixel in the original image [6].
 Dictionary Based Encoding: This encoding process is
also known as substitution encoding. In this process
the encoder maintain a data structure known as
‘Dictionary’. This is basically a collection of string. The
encoder matches the substrings chosen from the
original pixel and finds it in the dictionary; if a
successful match is found then the pixles is replaced
by a reference to the dictionary in the encoded file [6].
2.2.2 Lossy Compression:
Lossy Compression is generally used for image, audio,
video; where the compression process neglects some less
important data. The exact replica of the original file can’t
be retrieved from the compressed file. To decompress the
compressed data we can get a closer approximation of the
original file [6].
2.3 DATA COMPRESSION TECHNIQUES
Various kind of image compression algorithms have been
proposed till date, mainly those algorithms is lossless
algorithm. This paper examines the performance of the
Run Length Encoding Algorithm (RLE), Huffman Encoding
Algorithm and delta Algorithm. Performance of above
listed algorithms for compressing images is evaluated and
compared.
2.3.1 Run length encoding (RLE):
Run length encoding (RLE) is a method that allows data
compression for information in which pixels are repeated
constantly. The method is based on the fact that the
repeated pixel can be substituted by a number indicating
how many times the pixel is repeated and the pixel itself
[7]

The Run length code for a grayscale image is represented
by a sequence {Vi, Ri } where Vi is the intensity of pixel
and Ri refers to the number of consecutive pixels with the
intensity Vi as shown in Figure 1
Figure 1: Run Length Encoding [3]
This is most useful on data that contains many such runs
for example, simple graphic images such as icons, line
drawings, and animations. It is not useful with files that
don't have many runs as it could greatly increase the file
size. Run-length encoding performs lossless image
compression. Run- length encoding is used in fax machines
[3].
2.3.2 Huffman Coding
The Huffman coding algorithm [8] is named after its
inventor, David Huffman, who developed this algorithms a
student in a class on information theory at MIT in1950. It
is a more successful method used for text compression.
Huffman’s idea is to replace fixed-length codes (such as
ASCII) by variable-length codes, assigning shorter
codewords to the more frequently occurring symbols and
thus decreasing the overall length of the data. When using
variable-length codewords, it is desirable to create a
(uniquely decipherable) prefix- code, avoiding the need
for a separator to determine codeword boundaries.
Huffman coding creates such a code [5].
The Huffman algorithm is simple and can be described in
terms of creating a Huffman code tree. The procedure for
building this tree is:
 Start with a list of free nodes, where each node
corresponds to a symbol in the alphabet.
 Select two free nodes with the lowest weight from the
list.
 Create a parent node for these two nodes selected and
the weight is equal to the weight of the sum of two
child nodes.
 Remove the two child nodes from the list and the
parent node is added to the list of free nodes.
 Repeat the process starting from step-2 until only a
single tree remains.
After building the Huffman tree, the algorithm creates a
prefix code for each symbol from the alphabet simply by
traversing the binary tree from the root to the node, which
corresponds to the symbol. It assigns 0 for a left branch
and 1 for a right branch. The algorithm presented above is
called as a semi adaptive or semi- static Huffman coding as
it requires knowledge of frequencies for each pixel from
image. Along with the compressed output, the Huffman
tree with the Huffman codes for symbols or just the
frequencies of pixels which are used to create the Huffman
tree must be stored. This information is needed during the
decoding process and it is placed in the header of the
compressed file [5].
2.3.3 Delta encoding:
Delta encoding represents streams of compressed pixels
as the difference between the current pixel and the
previous pixel[9].
The first pixel in the delta encoded file is the same as the
first pixel in the original image. All the following pixels in
the encoded file are equal to the difference (delta)
between the corresponding value in the input image, and
the previous value in the input image [10].
In other words, delta encoding has increased the
probability that each pixel value will be near zero, and
decreased the probability that it will be far from zero. This
uneven probability is just the thing that Huffman encoding
needs to operate. If the original signal is not changing, or is
changing in a straight line, delta encoding will result in
runs of samples having the same value [10].
3. RELATED WORK:
[11] have reviewed state of the art techniques for lossless
image compression. These techniques were evaluated
experimentally using a suite of 45 image that repeated
several application domains. Among these techniques, the
researchers considered, the best compression ratios were
achieved by CALIC.
[6] examines the performance of a set of lossless data
compression algorithm, on different form of text data. A
set of selected algorithms are implemented to evaluate the
performance in compressing text data. A set of defined
text file are used as test bed. The performance of different
algorithms are measured on the basis of different
parameter and tabulated in this article. The article is
concluded by a comparison of these algorithms from
different aspects.
[2] have reviewed lossless data compression
methodologies and compares their performance. The
refreshers have find out that arithmetic encoding
methodology is very powerful over Huffman encoding
methodology. In comparison they came to know that
compression ratio of arithmetic encoding is better. And
furthermore arithmetic encoding reduces channel
bandwidth and transmission time.
[5] shows the comparison of different lossless
compression algorithm over text data. This text data were
available in the form of different kind of text file which

contain different text patterns. By considering the
compression time, decompression time and compression
ratio of all the algorithm, it can be derived that the
Huffman Encoding can be considered as the most efficient
algorithm among the selected ones.
In [3], the writers presents a survey of various types of
lossy and lossless image compression techniques and
analysis it.
This paper examines the performance of the Run Length
Encoding Algorithm (RLE), Huffman Encoding Algorithm
and delta Algorithm. Performance of above listed
algorithms for compressing images is evaluated and
compared on binary images. Grey level images and RGB
images.
4. METHODOLOGY:
In order to test the performance of above mentioned
compression algorithms e.g. the Run Length Encoding
Algorithm, Huffman Encoding Algorithm and Delta
Encoding, the algorithms were implemented and tested
with a various set of images. Performances of the
algorithm were evaluated by computing the compression
ratio and compression time. The performances of the
algorithms depend on the size of the source image and the
organization of different images types (Binary image, Grey
level images and RGB images) with different extensions
(.png and .jpg extensions) .
A chart is drawn in order to verify the relationship
between the images sizes after compression, the
compression time. An algorithm which gives an acceptable
saving percentage with minimum time period for
compression is considered as the best algorithm.
4.1 Experiment steps:
A. Prepare test bed: show appendix A for test bed
B. Run the program which developed using Java
language to evaluate the algorithms shown in
Figure 2.
C. Evaluation
Figure 2: screenshot from used software
4.2 Measurement Parameter:
Depending on the use of the compressed file the
measurement parameter can differ. Space and time
efficiency are the two most important factors for a
compression algorithm. Performance of the compression
algorithm largely depends on the redundancy on the
source data. So to generalize the test platform we used
same test files for all the algorithms [6]. The parameters
were as follows:
Compression Ratio: The ratio between the compressed file
and the original file.
Compression Factor: The ratio between the original file
and the compressed file. This is basically the inverse of the
Compression Ratio.
OR
Saving Percentage: The percentage of the size reduction of
the file after the compression.
Compression Time: The time taken by the algorithm to
compress the file calculated in milliseconds (ms).

5. RESULTS AND DISCUSSION:
The result found by implementing the three algorithms(
RLE, delta and Huffman) over the 7 test images are shown
in tables 1,2,3, the tables show compressed images size in
pixels, compression ratio, compression factor, saving
percentage and compression time in ms.
5.1 Results:
5.1.1 RLE algorithm:
Table 1: RLE algoritjim results
ImageNo.
imagetype
originalimagesize
"pixels"
compressedimage
size"pixels"
compressionratio
compressionfactor
savingpercentage
compressiontime
"ms"
1
Binary
8100 550 0.07 14.73 93% 0.25
2 16384 1938 0.12 8.45 88% 0.5
3
Greylevel
157960 274634 1.74 0.58 -74% 5.25
4 65536 111066 1.69 0.59 -69% 2
5
RGB
157960 928728 5.88 0.17 -488% 15.5
6 157960 928728 5.88 0.17 -488% 14.75
7 65536 239156 3.65 0.27 -265% 3.75
5.1.2 Delta Encoding algorithm:
Table 2: delta encoding results
ImageNo.
imagetype
originalimage
size"pixels"
compressed
imagesize
"pixels"
compressionratio
compression
factor
savingpercentage
compressiontime
"ms"
1
Binary
8100 267 0.03 30.34 97% 0.5
2 16384 125 0.01 131.07 99% 0.5
3
Greylevel
157960 124095 0.79 1.27 21% 15.5
4 65536 51450 0.79 1.27 21% 6.5
5
RGB
473880 344912 0.73 1.37 27% 34.5
6 473880 344912 0.73 1.37 27% 33
7 196608 113774 0.58 1.73 42% 12.5
5.1.3 Huffman Encoding:
Table 3: Huffman encoding results
ImageNo.
imagetype
originalimagesize
"pixels"
compressedimage
size"pixels"
compressionratio
compressionfactor
savingpercentage
compressiontime
"ms"
1
Binary
8100 1225 0.15 6.61 85% 0.25
2 16384 2984 0.18 5.49 82% 0.5
3
Grey
Level
157960 137025 0.87 1.15 13% 6.5
4 65536 57293 0.87 1.14 13% 2.75
5
RGB
473880 419885 0.89 1.13 11% 20.25
6 473880 419885 0.89 1.13 11% 19.75
7 196608 154124 0.78 1.28 22% 7.75
5.2 Discussion:
5.2.1Compression ratio comparison:
Table 4: Compression ratio comparison
imageNo.
RTEcompression
Ratio
Delta
compression
Ratio
Huffman
compression
Ratio
1 0.07 0.03 0.15
2 0.12 0.01 0.18
3 1.74 0.79 0.87
4 1.69 0.79 0.87
5 5.88 0.73 0.89
6 5.88 0.73 0.89
7 3.65 0.58 0.78

Figure 3: compression ratio comparison chart
From compression ratio comparison which presented in
table 4 and shown in Figure 3, we can conclude that the
Delta encoding is the best algorithm in all types of images
(Binary, Grey and RGB images), which similar to the
results mentioned in [12] in case of grey level images.
6 Saving percentage storage comparison:
Table 5: Saving percentage storage
imageNo.
RTEsaving
percentage
Deltasaving
percentage
Huffmansaving
percentage
1 93% 97% 85%
2 88% 99% 82%
3 -74% 21% 13%
4 -69% 21% 13%
5 -488% 27% 11%
6 -488% 27% 11%
7 -265% 42% 22%
Figure 4: Saving percentage storage
From saving percentage comparison which presented in
table 5 and displayed in Figure 4, we can show that:
 Delta encoding is the best compression algorithm
because it has the highest percentage saving value
 RTE algorithm is not suitable for grey level images and
RGB images, it just suitable for binary images because
the size of compressed images is raised in grey and
RGB images.
7 Compression time comparison:
Table 6: Compression time comparison
imageNo.
RLEcompression
time"ms"
Delta
compression
time"ms"
Huffman
compression
time"ms"
1 0.25 0.5 0.25
2 0.5 0.5 0.5
3 5.25 15.5 6.5
4 2 6.5 2.75
5 15.5 34.5 20.25
6 14.75 33 19.75
7 3.75 12.5 7.75

Figure 5: Compression time comparison
From compression time comparison which presented in
table 6 and shown in Figure 5, we can notice that:
 In case of binary image, the compression time was
very small in all techniques.
 In case of grey level and RGB images, Huffman
encoding is the best technique then RLE and the delta
encoding was the worst one.
5. CONCLUSION
In this paper we have compared three lossless data
compression algorithm RlE, Delta and Huffman encoding
using our test bed which consists of variant images types
and extension( binary images, grey level and RGB images)
and we evaluate the algorithms using different aspects
like, compression ratio, saving percentage storage and
compression time. We found that Delta encoding is the
best algorithm in case of compression ratio and saving
percentage storage, while Huffman encoding is the best
algorithm in case of compression time. In addition to that,
we found that RLE is not suitable for grey level and RGB
images.
In the future, more compression techniques will used and
compared over a large data set of images and video files
until reach to the best compression technique.
6. REFERENCES:
[1] M. Hasan and K. Nur, “A Lossless Image Compression Technique
using Location Based Approach,” vol. 1, no. 2, 2012.
[2] S. Porwal, Y. Chaudhary, J. Joshi, and M. Jain, “Data Compression
Methodologies for Lossless Data and Comparison between
Algorithms,” vol. 2, no. 2, pp. 142–147, 2013.
[3] S. Gaurav Vijayvargiya and R. P. Pandey, “A Survey : Various
Techniques of Image Compression,” vol. 11, no. 10, 2013.
[4] T. Europe, “An Introduction to Fractal Image Compression,”
Outubro, no. October, p. 20, 1997.
[5] M. Grossberg, I. Gladkova, S. Gottipati, M. Rabinowitz, P. Alabi,
T. George, and A. Pacheco, “A comparative study of lossless
compression algorithms on multi-spectral imager data,” Proc. -
2009 Data Compression Conf. DCC 2009, 2009.
[6] Arup Kumar Bhattacharjee, Tanumon Bej, and Saheb Agarwal,
“Comparison Study of Lossless Data Compression Algorithms for
Text Data n,” IOSR J. Comput. Eng., vol. 11, no. 6, pp. 15–19,
2013.
[7] F. Semiconductor, “Using the Run Length Encoding Features on
the MPC5645S,” pp. 1–8, 2011.
[8] P. O. F. T. H. E. I. R. E, “A Method for the Construction of
Minimum-Redundancy Codes *.”
[9] M. Dipperstein, “Adaptive Delta Coding Discussion and
Implementation.” [Online]. Available:
http://michael.dipperstein.com/delta/index.html.
[10] S. W. Smith, “Data Compression,” Sci. Eng. Guid. to Digit. Signal
Process., pp. 481–502, 1997.
[11] B. C. Vemuri, S. Sahni, F. Chen, C. Kapoor, C. Leonard, and J.
Fitzsimmons, “Losseless image compression,” Igarss 2014, vol. 45,
no. 1, pp. 1–5, 2014.
[12] N. Efford, “Digital image processing: a practical introduction using
java.” 2000.

Appendix A: Test bed
No. Image Image name extension Image size Image type
1 char_a .png 8100 pixels Binary image
2 chess .png
16384
pixels
Binary image
3 mattgrey .jpg
157960
pixels
Grey level image
4 matthead .png
65536
pixels
Grey level image

No. Image Image name extension Image size Image type
5 matthew1 .jpg
157960
pixels
RGB
6 matthew1 .png
157960
pixels
RGB
7 frplanet .png
65536
pixels
RGB

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