Lossless Huffman coding image compression implementation in spatial domain by using advanced enhancement techniques

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 613
Lossless Huffman coding image compression implementation in spatial
domain by using advanced enhancement techniques
Ali Tariq Bhatti1, Dr. Jung H. Kim2
1,2Department of Electrical & Computer engineering
1,2NC A&T State University, Greensboro NC USA
1atbhatti@aggies.ncat.edu, alitariq.researcher.engineer@gmail.com, ali_tariq302@hotmail.com
2kim@ncat.edu
Abstract: Images are basic source of information for
almost all scenarios that degrades its quality both in
visually and quantitatively way. Now–a-days, image
compression is one of the demanding and vast
researches because high Quality image requires larger
bandwidth. Raw images need larger memory space.
In this paper, read an image of equal dimensional size
(width and length) from MATLAB. Initialize and extract
M-dimensional vectors or blocks from that image.
However, initialize and design a code-book of size N for
the compression. Quantize that image by using Huffman
coding Algorithm to design a decode with table-lookup
for reconstructing compressed image of different 8
scenarios. In this paper, several enhancement techniques
were used for lossless Huffman coding in spatial domain
such as Laplacian of Gaussian filter. Use laplacian of
Gaussian filter to detect edges of lossless Huffman coding
best quality compressed image(scenario#8) of block size
of 16 and codebook size of 50. Implement the other
enhancement techniques such as pseudo-coloring,
bilateral filtering, and water marking for the lossless
Huffman coding c based on best quality compressed
image. Evaluate and analyze the performance metrics
(compression ratio, bit-rate, PSNR, MSE and SNR) for
reconstructed compress image with different scenarios
depending on size of block and code-book. Once finally,
check the execution time, how fast it computes that
compressed image in one of the best scenarios. The main
aim of Lossless Huffman coding using block and
codebook size for image compression is to convert the
image to a form better that is suited for analysis to
human.
Keywords:- Huffman coding, Bilateral, Pseudo-
coloring, Laplacian filter, Water-marking
1. Image Compression
Image compression plays an impassive role in memory
storage while getting a good quality compressed image.
There are two types of compression such as Lossy and
Lossless compression. Huffman coding is one of the
efficient lossless compression techniques. It is a process
for getting exact restoration of original data after
decompression. It has a lower Compression ratio In this
paper, Huffman coding is used. Lossy compression is a
process for getting not exact restoration of Original data
after decompression. However, accuracy of re-
construction is traded with efficiency of compression. It
is mainly used for image data compression and
decompression. It has a higher compression ratio. Lossy
compression [1][2] can be seen in fast transmission of
still images over the internet where the amount of error
can be acceptable.
Enhancement techniques mainly fall into two
broad categories: spatial domain methods and frequency
domain methods [9].
Spatial domain techniques are more popular
than the frequency domain methods because they are
based on direct manipulation of pixels in an image such
as logarithmic transforms, power law transforms, and
histogram equalization. However, these pixel values are
manipulated to achieve desired enhancement. But they
usually enhance the whole image in a uniform manner
which in many cases produces undesirable results [10].
2. Methodology
2.1 Huffman encoding and decoding process based
on block size and codebook for image compression
Step 1- Reading MATLAB image 256x256
Step 2:- Converting 256x256 RGB image to Gray-scale
level image
Step 3- Call a function that find the symbols for image
Step 4- Call a function that calculate the probability of
each symbol for image
Step 5- The probability of symbols should be arranged in
DESCENDING order, so that the lower probabilities are
merged. It is continued until it is deleted from the list [3]
and replaced with an auxiliary symbol to represent the
two original symbols.
Step6- In this step, the code words are achieved related
to the corresponding symbols that result in a
compressed data/image.
Step7- Huffman code words and final encoded Values
(compressed data) all are to be concatenated.
Step8- Huffman code words are achieved by using final
encoding values. This may require more space than just

the frequencies that is also possible to write the Huffman
tree on the output
Step9-Original image is reconstructed in spatial domain
which is compressed and/or decompression is done by
using Huffman decoding.
Step 10-Compressed image applied on Huffman coding
to get the better quality image based on block and
codebook size.
Step 11- Recovered reconstructed looks similar to
original image.
Step 12: Implement Laplacian of Gaussian 5x5 filtering
for lossless Huffman coding compressed image
Step 13: Implement Pseudo coloring for lossless
Huffman coding compressed image
Step 14: Implement Bilateral filtering for lossless
Huffman coding compressed image
Step 15: Implement Water marking for lossless Huffman
coding compressed image
Figure 1 Block diagram
2.2 Different scenarios
There are 8 different scenarios for image compression
using lossless Huffman coding based on block and
codebook size.
Figure 2 Original image (RGB to Gray-scale)
Scenario#8 Size of Block=M=16, and Size of
Codebook=N=50 (16X50)
Figure 3 Reconstructed Image of 16X50

Scenario#8 is the best one for better image quality which
is block size of 16 and codebook size of 50
2.3 Performance Metrics
There are following performance metrics used for image
compression of original and reconstructed image such as
(a) Bit Rate:
Bit Rate is defined as
(1)
(2)
The units for Bit Rate is bits/pixel.
(b) Compression Ratio:
Compression Ratio is defined as:

(3)
Compression Ratio is Unit-less.
(c) SNR:
SNR (Signal-To-Noise Ratio) is defined as
(4)
(d) MSE:
The Mean Square Error (MSE) is the error metric used to
compare image quality. The MSE represents the
cumulative squared error between the reconstructed(Yi)
and the original image(Xi).
(5)
(e) PSNR
Peak Signal-to-Noise Ratio short as PSNR, is an
engineering term for the ratio between the maximum
possible power of a signal and the power of corrupting
noise that affects the fidelity of its MSE representation.
(6)
Table 1 Performance metrics for lossless Huffman
coding for first image
2.4 Probabilities for the best quality compressed
image
In this paper, the block size of 16 and codebook size of
50 shows a better quality image than other scenarios .
Therefore, the probabilities:
Probabilities for codebook size of 25 and 50 are as:
prob =
Columns 1 through 13
0.0031 0.0062 0.0092 0.0123 0.0154 0.0185
0.0215 0.0246 0.0277 0.0308 0.0338 0.0369
0.0400
0.0431 0.0462 0.0492 0.0523 0.0554 0.0585
0.0615 0.0646 0.0677 0.0708 0.0738 0.0769
ent = 4.3917
prob =
0.0008 0.0016 0.0024 0.0031 0.0039 0.0047
0.0055 0.0063 0.0071 0.0078 0.0086 0.0094
0.0102
0.0110 0.0118 0.0125 0.0133 0.0141 0.0149
0.0157 0.0165 0.0173 0.0180 0.0188 0.0196
0.0204
0.0212 0.0220 0.0227 0.0235 0.0243 0.0251
0.0259 0.0267 0.0275 0.0282 0.0290 0.0298
0.0306
0.0314 0.0322 0.0329 0.0337 0.0345 0.0353
0.0361 0.0369 0.0376 0.0384 0.0392
ent = 5.3790
3. Laplacian of Gaussian filter and Pseudo-
coloring
Lossless Huffman coding reconstructed (best quality
compressed image of 16X50) using Laplacian of
Gaussian filter 5x5 kernal for figure 3 can be shown as

Figure 11 Laplacian filter for figure 3
Pseudo-color is one of an attractive technique for use on
digital image processing systems that is consequently
used when a single channel of data is available.
Figure 12 RGB intensity levels for figure 3
Figure 13 Plots of RGB over Gray levels for figure 3
Figure 14 Pseudo-colored image for figure 3
Figure 15 Pseudo coloring by sinusoids
Figure 16 Second compressed Image 16x50

Figure 17 Laplacian filter for second image 16x50
4. Bilateral Filtering
Tomasi and Manduchi [4] in 1998 introduced Bilateral
filtering technique. Therefore, the acceleration of the
computation speed is another interest for this type of
filtering presented as the SUSAN filter and also Bethel
neighborhood filter [5]. Therefore, [6][7][8] mentions
that the bilateral filter is also be a theoretical origin
which is known as Beltrami flow algorithm.
Figure 18 Bilateral filtering for figure 3
Figure 19 Bilateral filtering for second image
16x50
5. Water marking for lossless Huffman coding
Water marking is the process of inserting predefined
patterns into multimedia data in such a way to minimize
it’s quality degradation and hence remains at an
imperceptible level. It also informs whether that
information or data in that image is copyrighted or not.
However, PSNR is calculated for good reconstructed
compressed image based on block size of 16 and
codebook size of 50 (figure 3) for 8 bits in Water
marking technique.
Figure 20 Water-marking for second image using
1st bit
Psnr=9.0413
2nd bit

psnr =14.9908
3rd bit
psnr =20.9859
4th bit
psnr = 27.0473
5th bit
psnr = 33.0974
6th bit
psnr = 39.1044
7th bit
psnr =45.1095
8th bit
psnr = 51.1329
6. Motivation
(i)Good compressed image based on lesser block size of
16 and codebook size of 50 saves memory space and less
time while sending images over the network without
excessively reducing the quality of the picture.

(ii)When size of block is smaller:
(a)Good quality reconstructed image results in a higher
PSNR and SNR.(b)Compression ratio decreases, Bit Rate
increase.
(iii)Lesser the entropy and more the average length, so
better will be the good quality image.
7. Objectives
(i) To store or transmit image in an efficient form and to
reduce its redundancy.
(ii)To reduce the storage quantity and the reconstructed
image similar to the original image.
(iii)The dimensional vectors or blocks for a codebook
size of 25 and 50 in eight scenarios for lossless Huffman
coding.
(iv)To implement lossless Huffman coding in pseudo-
coloring, bilateral filtering, and water-marking
techniques.
(v)To detect edges of compressed imaged using
Laplacian filter.
8. Contribution
(i)Simple and lower memory implementation
requirement.
(ii)To reduce the number of block size of image that has
to be validated experimentally because it is labor-
intensive, costly and time-consuming.
(iii)Developed to solve in file compression, multimedia,
and database applications maintained by google servers.
9. Future Scope
Future scope is that the visibility of lossless Huffman
coding to use in other advance image enhancement
techniques.
10. Conclusion
Lossless Image compression such as Huffman coding
provides solution to this problem in this paper. Lossless
Huffman coding on block size of 16 and codebook
size of 50 in spatial domain is implemented to solve
the problem of good quality compressed image. A good
quality compressed image with lesser memory
requirement within a minimum bandwidth(lesser time)
to get more storage memory space.
(a) Good quality image with Lower compression ratio.
(b) Higher PSNR.
(c) Higher SNR.
(d) Lower MSE
(e) Lower entropy and more the Average Length.
Image enhancement features such as Laplacian of
Gaussian filter 5x5 kernal for lossless Huffman coding is
used for detection of edges of the compressed image.
Pseudo-coloring is useful for lossless Huffman coding
because the human eye can distinguish between millions
of colours but relatively few shades of gray. However,
Bilateral filtering is an efficient, non-iterative scheme for
texture removal. It can also do edge-preserving and
noise-reducing smoothing filter for lossless Huffman
coding.
Watermarking is one of the robust techniques that play
an important role whether that image is copy-right or
not.
Efficient and Effective communication of superior quality
digital images need reduction of memory space and less
bandwidth requirement.
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Huffman Decoding”, IEEE Trans, pp.936-939.
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Gray and Color Images", Proc. Int.Conf. Computer
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BIOGRAPHY
Ali Tariq Bhatti received his
Associate degree in Information
System Security (Highest
Honors) from Rockingham
Community College, NC USA, B.Sc.
in Software engineering (Honors)
from UET Taxila, Pakistan, M.Sc
in Electrical engineering
(Honors) from North Carolina
A&T State University, NC USA, and currently pursuing
PhD in Electrical engineering from North Carolina A&T
State University. Working as a researcher in campus and
working off-campus too. His area of interests and
current research includes Coding Algorithm, Networking
Security, Mobile Telecommunication, Biosensors, Genetic
Algorithm, Swarm Algorithm, Health, Bioinformatics,
Systems Biology, Control system, Power, Software
development, Software Quality Assurance,
Communication, and Signal Processing. For more
information, contact Ali Tariq Bhatti
alitariq.researcher.engineer@gmail.com.
Dr. Jung H. Kim is a professor in
Electrical & Computer engineering
department from North Carolina
A&T State University. His research
interests include Signal Processing,
Image Analysis and Processing,
Pattern Recognition, Computer
Vision, Digital and Data
Communications, Video
Transmission and Wireless
Communications.

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