ABSTRACT Lossless image compression is needed in many fields like medical imaging, telemetry, geophysics, remote sensing and other applications, which require exact replica of original image and loss of information is not tolerable. In this paper, a near lossless image compression algorithm based on row by row classifier with encoding schemes like Lempel Ziv Welch (LZW), Huffman and Run Length Encoding (RLE) on color images is proposed. The algorithm divides the image into three parts R, G and B, apply row by row classification on each part and result of this classification is records in the mask image. After classification the image data is decomposed into two sequences each for R, G and B and mask image is hidden in them. These sequences are encoded using different encoding schemes like LZW, Huffman and RLE. An exhaustive comparative analysis is performed to evaluate these techniques, which reveals that the proposed algorithm have smaller bits per pixel (bpp) than simple LZW, Huffman and RLE encoding techniques

Dr Ramandeep Kaur

Sukhjeet K Ranade

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Ramandeep Kaur Int. Journal of Engineering Research and Applications                      www.ijera.com 
ISSN : 2248-9622, Vol. 4, Issue 8( Version 3), August 2014, pp.56-60 
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Comparative Analysis of Lossless Image Compression Based On 
Row By Row Classifier and Various Encoding Schemes on Color 
Images 
 
Ramandeep Kaur, Dr. Sukhjeet K. Ranade 
M.Tech (CSE), Department of Computer Science, Punjabi University, Patiala. 
Assistant Professor, Department of Computer Science, Punjabi University, Patiala. 
 
ABSTRACT  
Lossless image compression is needed in many fields like medical imaging, telemetry, geophysics, remote 
sensing and other applications, which require exact replica of original image and loss of information is not 
tolerable. In this paper, a near lossless image compression algorithm based on row by row classifier with 
encoding schemes like Lempel Ziv Welch (LZW), Huffman and Run Length Encoding (RLE) on color images 
is proposed. The algorithm divides the image into three parts R, G and B, apply row by row classification on 
each part and result of this classification is records in the mask image. After classification the image data is 
decomposed into two sequences each for R, G and B and mask image is hidden in them. These sequences are 
encoded using different encoding schemes like LZW, Huffman and RLE. An exhaustive comparative analysis is 
performed to evaluate these techniques, which reveals that the proposed algorithm have smaller bits per pixel 
(bpp) than simple LZW, Huffman and RLE encoding techniques. 
 
I. INTRODUCTION 
In today era of computer industry, the major 
challenges are to minimize the time of transmission 
data over network and reduce the storage space. 
Transmission time can be reduced significantly by 
reducing the size of data files. A data file can be text, 
graphic images, audio, video or algorithms. Reducing 
the size of data files also minimize the storage 
capacity or space requirement on the computer. In 
many fields like medical imaging, remote sensing, 
military affairs and scientific research, both low 
storage consumption and high image quality is 
required. Therefore lossless and near lossless 
compression methods are strongly needed. Bit-plane 
encoding, Run Length Encoding (RLE), Huffman, 
Lempel Ziv Welch (LZW), arithmetic encoding and 
lossless predictive encoding are all popular lossless 
compression methods [1].Data compression is the 
technique to reduce the size of data files by reducing 
redundancy, which helps to decrease data storage 
requirements and hence minimize time and 
communication cost [2]. It can also be defined as the 
method of encoding rules that allow substantial 
reduction in the total number of bits to store or 
transmit a file [3]. Image compression is the 
application of data compression on digital image. 
Image compression is the technique to reduce 
redundant and irrelevant image data in order to store 
or transmit data over network in very efficient form. 
Image compression can be of two types, lossless 
image compression and lossy image compression. If 
the process of redundancy removing is reversible i.e.  
 
the exact reconstruction of the original image can be 
achieved then it is known as lossless image 
compression [4]. Lossless image compression 
methods used in applications like medical imaging, 
scientific images, satellite imaging, artificial images 
remote sensing and forensic analysis. On the other 
side lossy compression methods are used in web 
browsing, photography, image editing and printing 
[4-6]. In lossy compression there is a problem of 
compression artifacts, because it works on low bit 
rate. Yang and Bourbakis presented a paper to 
discuss the comparison of various lossless 
compression schemes depending upon their 
performance. Lossless image compression can 
always be modeled as a two stage procedure first one 
is décor-relation and second is entropy coding [4]. A 
new compression scheme based on sub block 
interchange (SBI) was proposed by Ng and Cheng 
[7].  When this scheme is compared with Burrows 
and Wheeler Transformation (BWT) [8], SBI gains a 
little bit improvement in the compresses ratio but 
have a greater improvement in the compression 
speed. BWT takes much longer time for compression 
than GIF because input data requires a sorting 
procedure based on quick sort algorithm [8]. 
Triantafyllidis and Strintzis presented a technique for 
the implementation of context based adaptive 
arithmetic entropy coding. A drawback of arithmetic 
coding of images using the above adaptive model is 
that it does not take into account the high amount of 
correlation between adjacent pixels [9]. Yao-hua et 
al. presented a paper in which a near-lossless image 
compression algorithm using classification, 
RESEARCH ARTICLE                          OPEN ACCESS 
Ramandeep Kaur Int. Journal of Engineering Research and Applications                      www.ijera.com 
ISSN : 2248-9622, Vol. 4, Issue 8( Version 3), August 2014, pp.56-60 
 www.ijera.com                                                                                                                                57 | P a g e  
information hiding and LZW is proposed. The 
algorithm takes advantage of the effects of the 
distribution of pixels on compression ratio. In the 
proposed algorithm, pixels of an image are classified 
row by row; pixels similar in value are gathered and 
the classification result is recorded in a mask image 
[1].In this paper lossless compression algorithm on 
color images having 24 bits per pixel (bpp) value is 
proposed using row by row classifier and different 
popular encoding schemes like LZW, Huffman and 
RLE. The proposed algorithm is a preprocessing 
stage followed by different encoding schemes which 
helps to reduce the size of image more than that of 
simple encoding schemes.  
 
 
II. THE PROPOSED ALGORITHM 
The proposed algorithm is mainly data re-
ordering process before applying the compression 
technique. To achieve better compression from a 
compression technique, input data may be processed 
in such a manner that correlation among the elements 
of the data can be increased. Here pixels of an image 
taken row by row are placed in two groups based on a 
threshold value. So that pixels having similar values 
fall in a same group. That is more correlation can 
achieve in the values of pixels. The proposed 
algorithm consists of 4 steps which are shown in Fig. 
1. These steps are discussed in following subsections.  
 
2.1 Classification row by row 
In this step classification will be performed Row 
by row based on determining the threshold by using 
histogram of each row. Classification is done to put 
pixels in classes and pixels in same class must have 
similar value. Here classes will be divided according 
to the threshold calculated using histogram of each 
row. The number of classes will be six, two for each 
Red ( 1Clr , 2Clr ), Green ( 1gCl , 2gCl ) and Blue 
( 1Clb , 2Clb ).After calculating threshold for each 
row, the pixels of each part (R, G and B) can be 
classified using below method: 
For Red part
1
2
( , ) ,
( , ) ,
Ir i j Clr
Ir i j Clr



  
( , ) ( )
( , ) ( )
Ir i j thr i
Ir i j thr i


 
 
Similarly we can classify Green and Blue parts. Ir 
represents Red part of original image, similarly Ib 
and Ig can be used for Blue and Green part. thr (i) 
represents threshold of each row of  Red part, 
similarly thg(i) and thb(i) can be used for Green and 
Blue part. 
i and j represents rows and column respectively and i, 
j = 0, 1, 2……………..S-1. ( 1Clr , 1gCl and 1Clb ) 
and ( 2Clr , 2gCl  and 2Clb ) represents classes 
belongs to great pixel value and small pixel value. 
Using threshold calculated above, a mask image Mr, 
Mg and Mb for red, green and blue part respectively 
can be formed of size S S  by allotting 0 and 1 for 
each pixel value as given below. 
Mask image for Red Part is   
( , ) 1,
( , ) 0,
Mr i j
Mr i j



     
( , ) ( )
( , ) ( )
Ir i j thr i
Ir i j thr i


 
Similarly we can generate mask images Mg and 
Mb for Green and Blue part respectively. Mr, Mg and 
Mb contain the information of classification results. 
 
2.2 Decomposition of image data 
After the classification of pixels, Original image 
is divided into six sequences, two for each Red, 
Green and Blue part and each sequence contains 
pixels of one class.  
Here the need to arrange the pixels of R,G and B 
part of image along the scan line by study row by row 
as given below : 
Ramandeep Kaur Int. Journal of Engineering Research and Applications                      www.ijera.com 
ISSN : 2248-9622, Vol. 4, Issue 8( Version 3), August 2014, pp.56-60 
 www.ijera.com                                                                                                                                58 | P a g e  
Dr={Ir (0, 0), Ir (0, 1), Ir (0, 2)…………..Ir (0, S-1) 
……………………………………………………… 
Ir (S-1, 0), Ir (S-1, 1)…………..……..Ir (S-1, S-1)} 
Similarly we can decompose Green (Dg) and Blue 
(Db) part of Image. 
Red part will be decomposed into two sequences 1dr  
and 2dr  based on threshold calculating in step first, 
method performed is given below. 
1
2
( , ) ,
( , ) ,
Ir i j dr
Ir i j dr



( , ) ( )
( , ) ( )
Ir i j thr i
Ir i j thr i


or
or
 
( , ) 1
( , ) 0
Mr i j
Mr i j


 
Similarly we can calculate above for Green and Blue 
part. Where ( 1dr , 2dr ), ( 1dg , 2dg ), and ( 1db , 2db ) 
represents subsequences for Dr, Dg and Db 
respectively and contains the pixels with great pixel 
value and small pixel value. 
 
2.3 Hiding the mask image 
Before hiding the mask image, the six 
decomposed sequences are combined into two 
sequences d1 and d2 depending upon their value 
calculated during decomposition. 
d1 = [ 1dr 1dg 1db ] and d2 = [ 2dr 2dg 2db ] 
For decoding process mask images Mr, Mg and 
Mb will be required and these mask images required 
extra storage space. To reduce this mask images will 
be hide in least significant bit (LSB) of d1 and d2. 
We need to arrange the mask images Mr, Mg and Mb 
according to scan line so that we can provide the 
exact value to LSB of exact pixel of the sequences d1 
and d2. 
Now we will arrange the pixels of Mr, Mg and 
Mb along the scan line, we get a sequence of binary 
value Br, Bg and Bb as given below: 
Br= {Mr (0, 0), Mr (0, 1), Mr (0, 2)…….Mr (0, S-1) 
………………………………………………………. 
Mr (S-1, 1), Mr (S-1, 2)……………..Mr (S-1, S-1)} 
And this approach will be similar to Bg and Bb. Now 
we hide the first element for Br i.e. Mr (0, 0) into 
LSB of first element of d1, then the second element 
Mr (0, 1) into second element of d1 and the process 
will be followed accordingly. When all the elements 
of d1 are used then elements of d2 will be used to 
hide the rest elements of mask image.  
To hide the mask image, we check the LSB of each 
element of d1 and d2. If the LSB of element or 
unassigned byte variable is 1, then we perform the 
“OR” operation with binary value of the element with 
1 otherwise perform “AND” with 254. 
& 254,
|1,
El
El
El

 
              
if
if
 
( ) 0
( ) 1
LSB El
LSB El


 
And after performing this operation, result will 
be stored in two new sequences S1 and S2 as shown 
in Fig. 1. 
2.4 Encoding and decoding using LZW, Huffman 
and RLE     and getting LSB from sequences  
Now these two sequences S1 and S2 will be 
encoded and decoded using LZW, Huffman and 
RLE. LZW is an error free compression approach and 
helps to remove spatial redundancy present in an 
image [4]. LZW assigns fixed length code word to 
variable length sequence of source symbols; it does 
not require prior knowledge of the probability of the 
occurrence of the symbols to be encoded. LZW is a 
dictionary based compression algorithm. This 
compression algorithm is used in many imaging file 
format GIF, TIFF and PDF [1]. 
Huffman coding is entropy coding algorithm and 
is used for lossless data compression. It is a form of 
statistical coding applicable to many form of data 
transmission like text file, program files 
Run length encoding is a very simple form of 
data compression in which runs of data (that is, 
sequences in which the same data value occurs in 
many consecutive data elements) are stored as a 
single data value rather than as the original run. This 
is most useful on data that contains many such runs 
for example, simple graphic images such as icons, 
line drawings, and animations. Run-length encoding 
performs lossless data compression and is well suited 
to palette-based bitmapped images such as computer 
icons. It does not work well at all on continuous-tone 
images such as photographs, although JPEG uses it 
quite effectively on the coefficients that remain after 
transforming and quantizing image blocks. It is also 
used in fax machines. 
After the decoding process we will get LSB from 
sequences S1 and S2 and mask images Mr, Mg and 
Mb will be extracted accordingly. After applying all 
the above step in reverse process the original image 
will be reconstructed. 
 
III. EXPERIMENTAL RESULT AND 
COMPARATIVE ANALYSIS 
The above algorithm is performed on color images of 
size S S having 24bpp value using “Matlab” 
version 7.14.0.739. Some of the popular test images 
used are “Airplane”, “Baboon”, “Barbara”, “House”, 
“Leena”, “Livingroom”, “Pepper”, and “Tree”. From 
original image R, G and B part extracted first and 
then row by row classification and decomposition is 
performed which is followed by different encoding 
and decoding schemes. Fig. 2 shows the original 
image of “Leena” its mask images and reconstructed 
image. 
Fig. 2. Original Image, Mask Image (R, G and B)  
and Reconstructed Image 
Ramandeep Kaur Int. Journal of Engineering Research and Applications                      www.ijera.com 
ISSN : 2248-9622, Vol. 4, Issue 8( Version 3), August 2014, pp.56-60 
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Fig. 2. Original Image, Mask Image (R, G and B) and 
Reconstructed Image 
 
 
(a)                                      (b) 
 
 
(c)                                      (d) 
 
 
(e)                                        (f) 
 
 
(g)                                       (h) 
(a)Airplane; (b) Baboon; (c) Barbara; (d) House;        
(e) Leena; (f) Livingroom; (g) Peppers;   (h) Tree; 
Fig. 3. The Popular Test Images 
 
The bpp value of the test images using the 
proposed algorithm with different encoding schemes 
can be analyzed through graph as shown in Fig. 4. 
Average bpp value calculated using simple RLE is 
20.36625 and using the proposed algorithm with RLE 
is 16.88 and in LZW it is 23.4975 and 20.31375 and 
in case of Huffman the value is 22.14 and 
19.42625.From this we conclude that the image has 
smaller bpp value when compressed through the 
proposed algorithm. During comparative analysis 
between different encoding schemes using algorithm, 
the results shows that RLE have smaller bpp value as 
compared to LZW and Huffman.  
During the process of mask image hiding there is 
a possibility of change of LSBs, due to which there 
will be a noise in reconstructed image. However 
PSNR value of test images shows that there is very 
significant change in the quality of reconstructed 
images. It is very difficult to notice from naked eyes 
any difference between original image and 
reconstructed image. Table 1. shows PSNR values of 
test images 
 
 
Ramandeep Kaur Int. Journal of Engineering Research and Applications                      www.ijera.com 
ISSN : 2248-9622, Vol. 4, Issue 8( Version 3), August 2014, pp.56-60 
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Fig. 4.  The bpp Comparison of Image Using Different Methods 
Table 1.  PSNR of Test Images 
 
 
IV. CONCLUSION AND FUTURE 
WORK 
A near lossless compression algorithm using row 
by row classification, mask image hiding, 
decomposition and different encoding schemes like 
LZW,RLE and Huffman is proposed on color images. 
Using Above algorithm smaller bpp value is obtained 
as compared to simple LZW, RLE and Huffman 
encoding.  
During comparative analysis between these 
encoding schemes using discussed algorithm we 
concluded that RLE have smaller bpp value as 
compared to other encoding schemes. In future task, 
other encoding schemes other than above discussed 
encoding techniques can be used on this correlation 
based algorithm to reduce the size of compressed 
image. 
 
REFERENCES  
 [1] X. Yao-hua, T. Xiao-an and S. Mao-yin, 
“Image compression based on classification 
row by row and LZW encoding”. Congress 
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617-621.  
[2] N.Ranganathan, S.G.Romaniuk and K.R. 
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[3] M.-B. Lin, J.-F. Lee and G.E. Jan, “A 
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decompression algorithm and its hardware 
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[4] M.Yang and N.Bourbakis, “An overview of 
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[5] K. Sayood and K. Anderson, “A differential 
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[6] S.A. Hassan and M. Hussain, “Spatial 
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[7] K.S. Ng and L.M Cheng, “SUB-BLOCK 
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[8] M. Burrows and D.J.Wheeler, “A Block-
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Comparative Analysis of Lossless Image Compression Based On Row By Row Classifier and Various Encoding Schemes on Color Images

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Comparative Analysis of Lossless Image Compression Based On Row By Row Classifier and Various Encoding Schemes on Color Images

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