Abstract: Recent development in image processing allows us to apply it in different domains. Radiography image of weld joint is one area where image processing techniques can be applied. It can be used to identify the quality of the weld joint. For this the image has to be stored and processed later in the labs. In order to optimize the use of disk space compression is required. The aim of this study is to find a suitable and efficient lossless compression technique for radiographic weld images. Image compression is a technique by which the amount of data required to represent information is reduced. Hence image compression is effectively carried out by removing the redundant data. This study compares different ways of compressing the radiography images using combinations of different lossless compression techniques like RLE, Huffman

B Karthikeyan

B Venkatraman

V Vaithiyanathan

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Research Journal of Applied Sciences, Engineering and Technology 5(1): 167-170, 2013 
ISSN: 2040-7459; e-ISSN: 2040-7467 
© Maxwell Scientific Organization, 2013 
Submitted: May 06, 2012                        Accepted: June 08, 2012 Published: January 01, 2013 
 
Corresponding Author: B. Venkatraman, RESG, IGCAR, Kalpakkam, India-603102 
167 
 
Review on Lossless Image Compression Techniques for Welding Radiographic Images 
 
1B. Venkatraman, 2V. Vaithiyanathan and 2B. Karthikeyan 
1RESG, IGCAR, Kalpakkam, India-603102 
2School of Computing, SASTRA University, Thanjavur, India-613401 
 
Abstract: Recent development in image processing allows us to apply it in different domains. Radiography image 
of weld joint is one area where image processing techniques can be applied. It can be used to identify the quality of 
the weld joint. For this the image has to be stored and processed later in the labs. In order to optimize the use of disk 
space compression is required. The aim of this study is to find a suitable and efficient lossless compression 
technique for radiographic weld images. Image compression is a technique by which the amount of data required to 
represent information is reduced. Hence image compression is effectively carried out by removing the redundant 
data. This study compares different ways of compressing the radiography images using combinations of different 
lossless compression techniques like RLE, Huffman. 
 
Keywords: Bit plane, gray code, huffman, image compression, radiography images, RLE 
 
INTRODUCTION 
 
Image compression is the technique by which the 
storage requirements for images of larger sizes are met 
by reducing the space consumed and also the bandwidth 
requirement for the transmission of the images can be 
reduced. The image compressions are of two types: 
Lossy compression and Lossless compression: 
 
 The lossy image compression technique is the 
process in which the image is compressed with 
some loss of information that is present in the 
original data.  
 The lossless image compression is the process of 
compressing the image without any loss of 
information present in the original image.  
 
The significance of the process of compression is 
that it enables the user to store more images in the 
allocated memory space. For the radiography more 
weld images should be stored in the data base 
(Rajagopalan et al., 2004) to check the quality and 
correctness of the process. In case of transmission, the 
bandwidth of the channel can be effectively used due to 
the reduced size of the image (Holtz, 1993). 
Considering these advantages we implement the 
compression technique on radiological images that 
consume more space. The radiological images are used 
in specialized fields like building of aircrafts and other 
equipments used in mission critical projects that need 
assistance and expertise for various process of welding. 
The major disadvantage faced during these processes is 
the amount of memory space consumed in storing these 
images. In order to overcome this restriction, we 
compress the image such that there is no loss in data 
(virtually lossless) and the memory required for storing 
the image is reduced. This study discusses various 
lossless techniques on radiographic weld images using 
binary and gray code values. 
 
MATERIALS AND METHODS 
 
The lossless compression based on Run Length 
Coding and Huffman coding is effectively analyzed. 
The technique of Huffman Coding and Run Length 
Coding are variable length coding types and thus error-
free. Some variations of Huffman Coding can produce 
even better results like parallel Huffman is better than 
bit-by-bit serial Huffman Coding (Howard and Vitter, 
1992) and adaptive Huffman Coding is still preferred 
over these (Jeon et al., 1998). We can also observe that 
time consumed to process gray code image is lesser 
than time required to process binary images (Xiaoli et 
al., 2004). Other than the variations of Huffman coding 
the various types of Run Length Coding are also very 
effective for certain applications. For example, the Run 
Length Coding to compute the value of difference 
between two digits (Messom et al., 2002). We can use 
the Run Length coding for identification of patterns 
(Messom et al., 2002) like identifying the porosity and 
slag in the welded material. Here we consider the very 
basic Huffman Coding and Run length coding. The 
Huffman Code is an error free, prefix-free code. In 
Huffman coding short code words are assigned to input 
blocks with high probabilities and long code words to 
the ones with low probabilities. 
 
 
Res. J. Appl. Sci. Eng. Technol., 5(1): 167-170, 2013 
 
168 
 
 
Fig. 1: Example of an Image line and its corresponding runs 
 
A Huffman code is formed by adding together two 
least probable source symbols and repeating this 
process until there are only two symbols remaining. A 
tree thus obtained by this is called Huffman tree and the 
Huffman code refers to code assigned to each symbol 
(Ashraf and Akber, 2005) by traversing the tree. Run 
Length Coding is an easy and efficient compression 
method based on the assumption that the image is a 
long data sequence without change of pixel value 
contents. These sequences are described by their 
characteristic position and length of appearance of that 
sequence. Thus the RLE is a flat or one dimensional 
compression algorithm (Xiaoli et al., 2004). 
Hence before applying Run Length Coding 
Technique, we first convert the gray scale image or 
gray code converted image value of each pixel into a 
1xn matrix (column transformation) or nx1 matrix (row 
transformation). This is shown in Fig. 1. 
It was observed that the RLE with parallel inputs 
gave a maximum lossless compression up to 95.39% 
Xiaoli et al. (2004). Hence on similar basis, we first 
apply Huffman code to the pixel (source symbol), then 
apply RLE to obtain an effectively compressed image. 
The Run Length Coding is also used in certain 
other sectors of image processing like: pattern 
recognition, pattern detection, etc., such sectors use 
RLE in real-time applications like detecting the 
pathway of the  robot,  route  memorizing of  the robot,  
etc.,  Luo et al. (2009). We can also use this RLE in the 
division of image segmentation, in the area of edge 
detection to identify the background and foreground 
pixels of the image. The radiographic weld images used 
in this study were obtained from IGCAR, Kalpak am. 
 
RESULTS AND DISCUSSION 
 
In case1, we apply RLE to the input image, we 
then apply Huffman coding to   the   result obtained   
after applying RLE. This is shown in Fig. 2. In case3, 
we apply bit plane method and then apply RLE, 
Huffman like case1. This is shown in Fig. 3. 
 
 
 
 
 
Fig. 2: Steps for case 1 
 
In case 2, we find the gray code equivalent of all 
the pixels and then we apply the process involved in 
case 1. This is shown in Fig. 4. In case 4, we find the 
gray code equivalent of all the pixels and we apply the 
process involved in case 3. This is shown in Fig. 5: 
 
Algorithm: 
Step 1: Img1  Read the input image 
Step 2: Img_gr  GrayCode (Img1) 
Step 3: Img_RLE  RLE(Img_gr, Choice) 
Step 4: Img_Huff  HuffmanCode (Img_RLE) 
 
The above algorithm is applicable for case 2 and 
case 4. For case 1 and case 3 RLE should be applied to 
the input image directly. By passing (Step 2). 
The pixel values of the input image are extracted 
and they are converted to their corresponding gray 
code. The advantage of the gray code is that the 
immediate values will have only one bit change. This is 
exploited for compression purpose in the bit plane 
method for case 4. Since with the radiographic images 
the intensity values does not vary drastically, 
converting into gray code will give only one bit change 
with neighboring pixels.  
Run Length Encoding allows us to represent the 
continuous runs of pixel of the same values. Here 
instead of taking pixel value entirely for finding the run,  
individual bits of all the pixels are taken in case 1 and 
case 2. In case 3 and case 4, the bit plane method is 
applied i.e., bi
th bit of all the pixels are taken for finding 
the run. Huffman is variable length encoding scheme 
which allows us to represent the characters using 
variable length codes. The advantage is that the 
character with higher probability of occurrence will 
have least number of bits to represent it. 
Input
 image 
RLE Huffman 
Output
 image 
 
 
Res. J. Appl. Sci. Eng. Technol., 5(1): 167-170, 2013 
 
169 
 
 
 
 
 
Fig. 3: Steps for case 3 
 
 
 
 
 
Fig. 4: Steps for case 2 
 
 
 
 
 
 
Fig. 5: Steps for case 4 
 
Table 1: Results for different image before enhancement 
Images/ 
Cases 
Img 1 
(MB) 
Img 2 
(MB) 
Img 3 
(MB) 
Img 4
(MB)
Case 1 22.031 22.013  21.952 22.326
Case 2 21.907 22.061 21.765 22.059
Case 3 12.109  11.809  12.086 11.939
Case 4 10.470  10.537  10.241 10.448
 
Table 2: Results for different image after enhancement 
Images/ 
Cases 
Img 1 
(MB) 
Img 2 
(MB) 
Img 3 
(MB) 
Img 4
(MB)
Case 1 22.486  22.290 22.298 22.421
Case 2 22.661 22.555 22.624 22.593
Case 3 17.683 17.617 17.148 17.690
Case 4 16.988  16.674 16.638 16.827
 
In the Bit plane method the image is represented as 
sixteen layers. Here we assumed that sixteen bits are 
used for storing a pixel value. The pixels in the ith layer 
are represented by bi
th bit of the pixel. Since gray code 
is applied the variation in the bits of the neighboring 
pixel is very less. 
We compare the results for different radiography 
weld images which have been compressed by this 
technique and the results are tabulated in Table 1 and 2. 
The actual image size is 23.437 MB. 
 
CONCLUSION 
 
Thus an image was taken and compressed by two 
different techniques in four different ways. We interpret 
that the gray coded bit plane method (i.e., case 4) gives 
better results than the other three cases. The memory 
required for storing is higher as indicated in Table 2. 
This is because the enhanced image has more details 
than the image before enhancement. This can be 
observed by seeing the images Fig. 6 and 7. The case 4 
gives the highest compression rate than remaining 
cases. 
 
 
 
Fig. 6: Actual image before enhancement 
 
 
 
Fig. 7: Actual image after enhancement 
 
REFERENCES 
 
Ashraf, R. and M. Akbar, 2005. Absolutely lossless 
compression of medical images. Proceedings of 
27th Annual International Conference of the IEEE 
Engineering in Medicine and Biology Society, 
Shanghai, China, pp: 4006-4009. 
 Input
 image 
Bit plane RLE
Output
 image Huffman 
 Input
 image 
RLE
Output
image Huffman Gray code
 Input
 image 
Gray code RLE
Output
image 
Huffman Bit plane 
 
 
Res. J. Appl. Sci. Eng. Technol., 5(1): 167-170, 2013 
 
170 
Holtz, K., 1993. The evolution of lossless data 
compression techniques. Proceedings of the IEEE 
WESCON/'93 Conference Record, California, 
USA, pp: 140-145. 
Howard, P.G. and J.S. Vitter, 1992. Parallel lossless 
image compression using huffman and arithmetic 
coding. Proceedings of the IEEE Data 
Compression Conference DCC, Snowbird, Utah, 
pp: 299-308. 
Jeon, B., J. Park and J. Jeong, 1998. Huffman coding of 
DCT coefficients using dynamic codeword 
assignment and adaptive codebook selection. 
Signal Process. Image Commun., 12: 253-262. 
Luo, F., H. Zhihui, Y. Fei and S. Dongyuan, 2009. 
Route memorization in real-time data processing 
using run-length encoding. IV 2009: IEEE 
Intelligent Vehicle Symposium, Shaanxi, China, 
pp: 1354-1358. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Messom, C.H., S. Demidenko, K. Subramaniam and 
G.S. Gupta, 2002. Size/position identification in 
real-time image processing using run length 
encoding. Proceedings of the 19th IEEE 
Instrumentation and Measurement Technology 
Conference, Anchorage, AK, USA, pp: 1055-1059. 
Rajagopalan, C., B. Venkatraman, T. Jayakumar, P. 
Kalyanasundaram and R. Baldev, 2004. A novel 
tool for automated evaluation of radiographic weld 
images. 16th WCNDT 2004-World Conference on 
NDT, Aug 30-Sep 3, Montreal, Canada, pp: 1-7. 
Xiaoli, L., S. Krishnan and M. Ngok-Wah, 2004.  
A novel way of lossless compression of digital 
mammograms using grammar codes. Proceedings 
of the IEEE Canadian Conference on Electrical and 
Computer Engineering,  Ontario, Canada, pp: 
2085-2088. 
 
 


Review on Lossless Image Compression Techniques for Welding Radiographic Images

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Review on Lossless Image Compression Techniques for Welding Radiographic Images

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