20120140504016

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 146-159 © IAEME
146
COLOUR IMAGE COMPRESSION USING BLOCK TRUNCATION CODING
AND GENETIC ALGORITHM
T.M. Amarunnishad1
, Meekha Merina George2
1
Dept. of Comp. Sci. & Engg., TKM College of Engg., Kollam - 691005, Kerala, India.
2
Dept. of Comp. Sci. & Engg., TKM Inst. of Tech., Kollam - 691505, Kerala, India.
ABSTRACT
In this paper a colour image data compression method using block truncation coding (BTC)
and genetic Algorithm (GA) is presented. This compression technique reduces the computational
complexity and provides acceptable visual quality reconstructed images. BTC acts as the basic
compression technique but it exhibits two disadvantages such as the false contour and blocking
effect. Hence error diffused block truncation coding (EDBTC) is used to overcome the two issues.
The proposed system is made applicable for colour images. Colour images comprise three planes:
red, green, and blue. There are very high correlations between the images in these planes. This
motivates the use of one common bit plane to represent all three colour bit planes in the proposed
compression method for colour images. GA is used to make the common bit plane optimal.
Experimental results show that the proposed method provides acceptable visual quality reconstructed
images with high peak signal to noise ratio (PSNR) when compared to the conventional BTC and
EDBTC methods.
Keywords: Image Compression, Block Truncation Coding, Genetic Algorithm, Optimal Bit Plane,
Reconstructed Image, Peak Signal To Noise Ratio.
1. INTRODUCTION
Internet has become a very popular means of communication due the emergence and
flourishing of network technologies. In recent years, the development and demand of multimedia
product grows increasingly fast, contributing to insufficient bandwidth of network and storage of
memory device. Therefore, the theory of data compression becomes more and more significant for
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING
AND TECHNOLOGY (IJARET)
ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 5, Issue 4, April (2014), pp. 146-159
© IAEME: www.iaeme.com/ijaret.asp
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147
reducing the data redundancy to save more hardware space and transmission bandwidth. Images
form a significant part of data, its use in the internet continue to grow. This necessitates the need for
storing and transmitting huge amount of data. The purpose of image compression is to represent
images with less data in order to save storage space or transmission time.
The image compression techniques are categorized into two main classifications namely
lossy compression techniques and Lossless compression techniques [1]. Lossy data compression is
named for what it does. After one applies lossy data compression to a message, the message can
never be recovered exactly as it was before when compressed. When the compressed message is
decoded it does not give back the original message. As lossy compression cannot be decoded to yield
the exact original message, it is not a good method of compression for critical data, such as textual
data. It is most useful for Digitally Sampled Analog Data (DSAD) as it consists mostly of sound,
video, graphics, or picture files. Lossless data compression is also named for what it does. In a
lossless data compression file the original message can be exactly decoded. Lossless data
compression works by finding repeated patterns in a message and encoding those patterns in an
efficient manner. For this reason, lossless data compression is also referred to as redundancy
reduction. Because redundancy reduction is dependent on patterns in the message; it does not work
well on random messages. Lossless data compression is ideal for text [2].
We propose a colour image compression method utilizing the basic conventional block
truncation coding (BTC) method. This method has demonstrated in producing perceptually high
quality reconstructed images with high peak signal to noise ratio (PSNR) values. The BTC is a fast,
simple algorithm used for coding images at good quality and at low to moderate compression ratios.
The basic algorithm of BTC was developed by Delp and Mitchel [3] by incorporating ideas relative
to exploiting statistical moments in the context of image compression. Although the performance of
BTC is inferior to transform coding such as JPEG [4], it requires a significantly smaller
computational load and has gained popularity due to its practical usefulness for low cost applications
such as software based multimedia systems. Since its introduction, the BTC algorithm has been
modified in various ways and several different basic algorithms under the generic name BTC have
been appeared in literature [5] – [8]. All these BTC algorithms are simple, computationally fast and
capable in fine edge preservation. However, due to insufficient quantization levels, it produces
ragged edges in the reconstructed images. Another problem of BTC is its high bit rate. For a fixed
block size BTC, the bit rate is about 2 bits/pixel. There are BTC algorithms [9] – [10], which can
obtain a lower bit rate. However, the computation is complex so that it is hard to implement in real
time processing.
In this paper a BTC scheme, error diffused BTC (EDBTC) [11] is used as the basic
compression technique because of the fact that it overcomes the issues in conventional BTC [3]. This
compression technique is used for colour images by separating each pixel into R, G, and B planes
[12]. Genetic algorithm (GA) [13] is used to optimize the bit plane thus improving the visual quality
of the reconstructed image with a better PSNR values and reduced error.
The rest of the paper is organized as follows. Section 2 describes the BTC. The proposed
method is discussed in section 3. The results are illustrated in section 4, and section 5 concludes this
paper.
2. BLOCK TRUNCATION CODING
The BTC, a type of lossy image compression technique is a two level non-parametric binary
encoder based on moment preserving quantization that adapts to the local properties of the image.
The input image is partitioned in to non-overlapping pixel blocks of size n × n, and each block is
processed individually. In this process, the first moment (‫ݔ‬ҧ), second-moment (‫ݔ‬ଶതതത), and variance (ߪଶ
)

148
of the input block are calculated to yield the mean, high-mean and low-mean for further BTC
processing. The equations to compute the above are given below:
‫ݔ‬ҧ ൌ
ଵ
௠
∑ ‫ݔ‬௜
௠
௜ୀଵ (1)
‫ݔ‬ଶതതത ൌ
ଵ
௠
∑ ‫ݔ‬௜
ଶ௠
௜ୀଵ (2)
ߪଶ
ൌ ‫ݔ‬ଶ െ ሺ‫ݔ‬ሻଶ
(3)
Where m = n × n (say n = 4) and xi
= x1
, x2
, …. , xm
be the pixels in a block of the input image.
The BTC algorithm preserves the first two sample moments of each pixel block, when the
original block is substituted by its quantization levels. That is, every pixel in a block is represented
by either a high-mean or a low-mean of the block. Thus, the following two conditions are
maintained:
݉‫ݔ‬ҧ ൌ ሺ݉ െ ‫ݍ‬ሻܽ ൅ ‫ܾݍ‬ (4)
݉‫ݔ‬ଶതതത ൌ ሺ݉ െ ‫ݍ‬ሻܽଶ
൅ ‫ܾݍ‬ଶ
(5)
where m = n × n, and q denotes the number of pixels with values greater than ‫ݔ‬ҧ. The high-mean and
low-mean used to represent a block can be evaluated as follows:
ܽ ൌ ‫ݔ‬ഥ– ߪ ට
௤
௠ି௤
(6)
ܾ ൌ ‫ݔ‬ഥ ൅ ߪ ට
௠ି௤
௤
(7)
where the two variables ‘a’ and ‘b’ denote the low-mean and high-mean, respectively and ߪ is the
standard deviation.
The bit plane with values ‘0s’ and ‘1s’ is generated by the one-bit quantizer, which is used to
record the distribution of the two quantization levels, low-mean and high-mean defined by
‫,ݐݑ݌ݐݑ݋‬ ‫ݕ‬௜ ൌ ൜
ܾ ൌ 1, ݂݅ ‫ݔ‬௜ ൒ ‫ݔ‬௧௛
ܽ ൌ 0, ݂݅ ‫ݔ‬௜ ൏ ‫ݔ‬௧௛
(8)
Where ‫ݔ‬௧௛ is the threshold and is taken as the mean of the block ‫ݔ‬ҧ
The compressed image data contains mean, ‫ݔ‬ҧ and the standard deviation, ߪ of all the 4 × 4
blocks and the bit plane. Each input image is transmitted as bit plane consisting of ‘0s’ and ‘1s’
along with quantized information on the ‫ݔ‬ҧ and ߪ . At the receiver end, each image block is
reconstructed such that ‫ݔ‬ҧ and ߪ are preserved. For each pixel with value xi
, the output levels ‘a’ and
‘b’ are calculated using equations (6) and (7) and each image block is reconstructed by assigning
these values to pixels in accordance with ‘0s’ and ‘1s’ in the bit plane. In the bit planes there can be
blocks with all the values either ‘0’ or ‘1’, such blocks are visually continuous indicating no edges in
them. The sample mean, ‫ݔ‬ҧ is used to represent that block. So while reconstructing the image, such
blocks are given a reconstruction value equal to the mean, ‫ݔ‬ҧ.

149
Figure 1 illustrates an example of the BTC compression. The block size is assumed to be
4 × 4 pixels. The computed ‫ݔ‬ and ߪ values are 191 and 19, respectively. A bit ‘1’ is assigned to all
pixels with values greater than the mean and bit ‘0’ is assigned to pixels with values less than the
mean. Low-mean and high-mean are 183 and 206, respectively. The image is reconstructed by
replacing the bit ‘1s’ in the block with high-mean and bit ‘0s’ with low-mean.
‫;191=ݔ‬ ߪ =19
206 212 189 177 1 1 0 0 206 206 183 183
213 194 182 184 1 1 0 0 206 206 183 183
192 179 187 178 1 0 0 0 206 183 183 183
179 186 221 185 0 0 1 0 183 183 206 183
(a)Input image block (b) transmitted data (c) output image block
Fig. 1: Example of BTC compression for gray scale image
3. PROPOSED METHOD
For colour image compression, three bit planes are obtained for R, G and B planes from the
input colour image. Since there is a high correlation between the images in these planes, a common
bit plane is used to represent all three colour bit planes. The implementation of the method is
depicted in Fig 2. First the colour image is decomposed into the respective R, G, and B planes. Each
input colour plane is divided into 4 × 4 non-overlapping blocks of pixels and the block mean and
standard deviation are calculated. The average of the means of the three R, G, and B blocks is
calculated to use in the one-bit quantizer for the common bit plane generation. If the average of
three pixels of the respective R, G, and B planes is greater than the average mean value, the pixel in
the common bit plane is represented as bit ‘1’ otherwise ‘0’. At the receiving end, the high-mean and
low-mean are calculated for the R, G, and B planes separately by using the respective mean
and standard deviation. During reconstruction of the image, bit ‘1s’ in the bit plane are replaced by
the corresponding high-mean and bit ‘0s’ are replaced by the low-mean.

150
Fig. 2: Schematic of Colour image BTC
Decompose the image
into R,G,B planes
G plane:
calculate the mean and
std. (‫ݔ‬ீ,ߪீ)
B plane:
std. (‫ݔ‬஻,ߪ஻)
R plane:
std. (‫ݔ‬ோ,ߪோ)
Calculate the average of
means:
‫ݔ‬ோ , ‫ݔ‬ீ , ‫ݔ‬஻
Find the common bit
plane
Transmit common bit
plane and mean and std.
of R, G, B planes
At the receiver find ‘q’,
the no. of pixels in the bit
plane with value ‘1’
‫ݔ‬ோ ‫ݔ‬஻
‫ݔ‬ீ
‫ݔ‬ீ,ߪீ
‫ݔ‬஻,ߪ஻
‫ݔ‬ோ,ߪோ
Bit plane
values all 0/1?
Reconstruct the pixels for
the R,G,B planes by the
respective means of the
block
Reconstruct the pixels by
using equations (6) and
(7)
No
Yes
Reconstructed image
Input image

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3.1 Error Diffused Block Truncation Coding
The traditional BTC comes with two issues: false contour effect and blocking effect. To
reduce with these issues EDBTC [11] was used. The EDBTC exploits the inherent dithering
property of the error diffusion to overcome false contour problem. Moreover, the blocking effect can
also be eased by its error kernel, since both sides of a boundary between any pair of resulting image
blocks being correlated.
‫ݒ‬௜,௝ ൌ ‫ݔ‬௜,௝ ൅ ‫ݔ‬௜,௝
′
(9)
‫݁ݎ݄݁ݓ‬ ‫ݔ‬௜,௝
′
ൌ ∑ e୧ା୫, ୨ା୬ ൈ ek୫,୬ሺ୫,୬ሻ
݁௜,௝ ൌ ‫ݒ‬௜,௝ െ ‫ݕ‬௜,௝ (10)
‫݁ݎ݄݁ݓ‬ ‫ݕ‬௜,௝ ൌ ൜
‫ݔ‬௠௔௫, ݂݅ ݄௜,௝ ൌ 1
‫ݔ‬௠௜௡, ݂݅ ݄௜,௝ ൌ 0
ܽ݊݀ ݄௜,௝ ൌ ቊ
1, ݂݅ ‫ݒ‬௜,௝ ൒ ‫ݔ‬
0, ݂݅ ‫ݒ‬௜,௝ ൏ ‫ݔ‬
The variables ‫ݔ‬௜,௝ є X and ‫ݕ‬௜,௝ є Y denote the input pixel value and the EDBTC output,
respectively. The output ‫ݕ‬௜,௝ is substituted by either maximum ሺ‫ݔ‬୫ୟ୶ ሻ or minimum (‫ݔ‬௠௜௡) of the
block according to the bitmap ݄௜,௝ є H. The variable ek୫,୬ denotes the employed error kernel, which
is used to diffuse the error ݁௜,௝ to its neighboring pixels. Herein, the Floyd- Steinberg’s error kernel
[14] as shown in Fig 3 was employed, where the notation x denotes the current processing position.
The error at the boundary of a block should be diffused to its neighbouring blocks, thus the blocking
effect can be significantly eased.
An example is illustrated in Fig 4. The block size is assumed to be 4 × 4 pixels. The mean of
the block ‫ݔ‬ҧ is 191. The minimum pixel value and maximum pixel value are 177 and 221,
respectively. A bit 1 is assigned to all pixels with values greater than mean and bit 0 is assigned to
pixels with values less than the mean. The image is reconstructed by replacing the bit 1’s in the block
with 221 and bit 0’s with 177. The neighbouring pixels in the reconstructed image are constructed
using Floyd’s error kernel. An error is computed by taking the difference between original pixel
value and the corresponding reconstructed pixel value i.e 205 – 221 which is -16. Using Floyd’s
error kernel the pixel value 205 is calculated (212 + (7/16) x (-16) = 205). The EDBTC compression
method can be applied directly to colour images.
x 7/16
3/16 5/16 1/16
Fig. 3: Floyd’s error kerne

152
YES
NO
205 212 189 177 221 205 189 177
213 194 182 184 208 193 182 184
192 179 187 178 192 179 187 178
179 186 221 185 179 186 221 185
Gray scale image Reconstructed image
Fig. 4: Example of EDBTC
3.2 Genetic Algorithm
It is a fact that stronger individuals are likely to win a competition; GA utilizes this fact. GAs
are good at taking large, potentially huge search spaces and navigating them, looking for optimal
combinations of things, solutions one might not otherwise find in a lifetime. Flow chart of a typical
genetic algorithm [12] is shown in Fig 5.
Fig. 5: Flowchart of genetic algorithm
GA is based on Darwin’s theory of natural selection and it uses the survival of the fitness
strategy to find the optimal starting point from a population of candidate points. It uses a population
of possible solutions to the search space. Each solution is encoded in a string called a chromosome
Population of
chromosomes
Selection
Crossover
Mutation
StopTerminal
condition

153
(or genome). Chromosomes are evaluated for fitness on each generation (iteration); chromosomes
that are more fit have a high probability of surviving. Once the surviving population is chosen,
different “parent” chromosomes are combined to form “child” chromosomes. Chromosomes may
undergo mutation. A new generation is formed, the process is repeated. By selection, cross-over, and
mutation, GAs searches the solution space while creating stronger solutions over each generation.
Selection
A chromosome is presented as a combination of ‘1s’ or ‘0s’. The initial population of N
chromosomes is randomly generated. It is possible to preprocess the chromosomes to increase the
convergence rate of GAs with a better initialization. A subset of the population forms the mating
pool.
Crossover
Once the mating pool has been formed, genetic information between the 2 bit planes is
exchanged to create a new population. This process of exchange of information is the crossover
process. It is illustrated in Fig 6.
Parent 1 Parent 2 Child 1 Child 2
1 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1
0 1 1 1 0 0 0 0 0 1 1 1 0 0 0 0
0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1
0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0
Exchanging tails
Fig. 6: Illustration of Crossover
Mutation
Parent selection and crossover operation may result in somewhat identical individuals who may
not be the fittest. Thus the genetic algorithm can get stuck after sometime. New genetic information
is brought into population by simulating the process of mutation. Mutation is the random operation
for changing the fitness and is used sparingly. The mutation operator changes ‘1’ to ‘0’ and ‘0’ to ‘1’
with a small mutation probability ‘Pm’. It is illustrated in Fig 7.

154
Child 1 Child 2
1 0 0 1 1 1 1 1
0 1 1 1 0 0 0 0
1 0 1 0 0 0 0 1
0 0 0 1 1 1 1 0
Mutated
Fig. 7: Illustration of Mutation
Fitness function
The fitness function basically determines which possible solutions get passed on to multiply
and mutate into the next generation of solutions. This is usually done by analyzing the "genes,"
which hold some data about a particular solution to the problem you are trying to solve. The fitness
function will look at the genes and make some qualitative assessment, returning a fitness value for
that solution. The rest of the genetic algorithm will discard any solutions with a "poor" fitness value
and accept any with a "good" fitness value. In short: the goal of a fitness function is to provide a
meaningful, measurable, and comparable value given a set of genes. Two fitness function f and pi are
defined to calculate the fitness value:
݂ ൌ
ଵ
ெௌா
(11)
‫݌‬
௜
೑ሺ೒೔ሻ
∑ ೑ሺ೒೔ሻೖಿ
ೕసభ
݇ (12)
where MSE is the mean square error; k is kth
iteration; gi is the preserved gene; N is the amount of
preserved chromosomes in the mating pool
‫ܧܵܯ‬ ൌ
ଵ
ெൈே
∑ ∑ ሾ‫ܫ‬ሺ݅, ݆ሻ െ ‫ܫ‬′ሺ݅, ݆ሻሿଶே
௝ୀଵ
ெ
௜ୀଵ (13)
where I is the input image and I’ is the output image
4. RESULTS AND DISCUSSIONS
In this section, simulation results are illustrated to demonstrate the effectiveness of the
proposed method. The performance of the proposed system has been tested with a set of gray scale
and colour images, ten each, and each of size 256 × 256. For comparison, BTC [3] and EDBTC [11]
are also implemented. The commonly used measures in a variety of image coding system, mean

155
absolute error (MAE), root mean square error (RMSE), and PSNR have been used to evaluate the
performance of the proposed method. They are defined as follows:
‫ܧܣܯ‬ ൌ
ଵ
௑௒
∑ ∑ ሾ|‫ܫ‬ሺ‫,ݔ‬ ‫ݕ‬ሻ െ ‫ܫ‬′ሺ‫,ݔ‬ ‫ݕ‬ሻ|ሿ
௬
௝ୀଵ
௫
௜ୀଵ (14)
ܴ‫ܧܵܯ‬ ൌ ሾ
ଵ
௑௒
∑ ∑ ሾ‫ܫ‬ሺ‫,ݔ‬ ‫ݕ‬ሻ െ ሺ‫,ݔ‬ ‫ݕ‬ሻሿଶ
ሿଵ/ଶ௬
௝ୀଵ
௫
௜ୀଵ (15)
ܴܲܵܰሺ݀‫ܤ‬ሻ ൌ 10݈‫݃݋‬ଵ଴ ൤
௠௔௫ೣ,೤ூሺ௫,௬ሻ
ோெௌா
൨
ଶ
(16)
Where Iሺx, yሻ is the original image, I′(x,y) is the reconstructed image, and X×Y is the dimension of
the images.
A lower value of RMSE means lesser error in the reconstruction, and as seen from the inverse
relation between the RMSE and PSNR, this translates to a high value of PSNR. Logically, a higher
value of PSNR is preferable because of the higher signal to noise ratio. Here, the signal is the
original image and the noise is the error in reconstruction. So a compression method having lower
RMSE and corresponding high PSNR values could be recognized as a better scheme.
Tables 1 – 2 show the performance results of the images decoded by the BTC, EDBTC, and
the proposed method for both the grayscale and the colour images. From the test results, it is
observed that the performance of the proposed method with the use of GA is better with lower
RMSE and higher PSNR values than the BTC [3] and EDBTC [11] for all the grayscale and colour
test images. It is illustrated by the clustered column chart shown in Fig 8 that compares the average
values of RMSE and PSNR for all the test images, both the gray scale and colour images for the
proposed and other methods. The average improvements in PSNR of the proposed method with the
use of GA for the grayscale and colour test images over the EDBTC [11] are 6.66 and 6.51 dB,
respectively. However the EDBTC [11] performs only slightly better than the BTC [3] with an
average improvements in PSNR values of 0.48 and 0.49 dB, respectively, for the grayscale and
colour test images. It indicates that the proposed method with GA performs much better than the
BTC [3] and EDBTC [11] methods for both the grayscale and colour images.
Figure 9 shows the reconstructed images by the proposed method and BTC [3] and EDBTC
[11] methods for both the grayscale and colour images. It is to note that, though visually, all the test
images have been reconstructed well, the proposed method with GA has shown comparatively much
better performance with better PSNR values compared to the BTC [3] and EDBTC [11] methods for
both the gray scale and colour images. The raggedness and the jagged appearance are greatly reduced
from the reconstructed images by the proposed method with GA. Also, the ringing artifacts at sharp
edges are considerably minimized in the reconstructed image.
The proposed method based on the BTC has some invaluable characteristics, which make it
in some practical applications a useful alternative to transform coding such as JPEG [6]. Transform
coding has some real problems when storing high detail regions of an image at an acceptable bit rate.
The BTC based proposed method has the ability to preserve edges and other such details of an image
while maintaining an acceptable bit rate. It would be inappropriate to compare the proposed BTC
based method with JPEG or any other

156
Table 1: MAE, RMSE, and PSNR values for BTC [3], EDBTC [11] and proposed methods for gray
scale images
Table 2: MAE, RMSE, and PSNR values for BTC [3], EDBTC [11] and proposed methods for
colour images
Images
BTC EDBTC Proposed method
MAE RMSE PSNR MAE RMSE PSNR MAE RMSE PSNR
Cameraman 4.30 9.19 28.78 4.03 8.79 29.17 1.53 3.72 36.66
Barbara 5.58 8.96 28.70 4.79 8.14 29.60 1.14 3.16 37.83
Elaine 3.93 6.48 31.40 3.59 6.30 31.65 2.17 4.39 34.79
Lena 4.22 7.47 30.07 3.82 7.11 30.49 2.17 4.19 35.08
Lighthouse 5.72 9.36 28.54 8.62 5.14 29.24 3.04 1.05 38.29
Boat 5.19 8.68 28.94 4.76 5.23 29.40 1.99 4.24 34.16
House 3.17 6.12 31.87 2.86 5.76 32.39 2.19 4.25 35.03
Coin 2.09 5.31 33.58 1.92 5.08 33.97 0.87 2.23 41.11
Airfield 7.49 12.34 26.30 6.87 11.64 26.81 0.98 3.42 37.43
Crowd 6.06 10.10 28.04 5.58 9.67 28.43 1.30 3.47 37.31
AVERAGE 4.78 8.40 29.63 4.69 7.94 30.11 1.54 3.61 36.77
Images
BTC EDBTC Proposed method
MAE RMSE PSNR MAE RMSE PSNR MAE RMSE PSNR
Eagle 1.36 4.96 34.18 1.31 4.65 34.74 0.79 2.22 40.38
Baboon 6.55 8.95 29.09 5.81 8.50 29.54 0.41 2.02 41.98
Barbara 5.52 8.73 29.31 4.96 8.19 29.86 0.56 2.36 40.65
Peppers 2.96 5.66 33.06 2.68 5.42 33.44 2.09 3.86 36.39
Lena 4.11 7.23 30.95 3.71 6.88 31.37 1.85 4.00 36.07
Cat 8.31 12.33 26.30 7.32 11.59 26.84 0.97 2.88 38.93
Football 3.33 5.41 33.46 2.96 5.18 33.84 2.09 4.26 35.53
Tulips 6.74 10.69 27.55 6.23 10.21 27.94 0.96 3.20 38.02
Tiffany 2.97 5.06 34.04 2.72 4.84 34.42 1.88 4.08 35.91
Zelda 3.84 6.14 32.35 3.40 5.79 32.87 1.82 3.84 36.43
AVERAGE 4.57 7.52 31.03 4.11 7.13 31.52 1.34 3.27 38.03

157
Fig. 8: Average values of RMSE and PSNR of proposed, BTC [3], and
EDBTC [11] methods for all the gray scale and colour test images
Original image BTC Original image BTC
EDBTC Proposed method EDBTC Proposed method
(a) Gray scale image - Cameraman (b) Colour image - Baboon
Fig. 9: Reconstructed images by the proposed, BTC [3], and EDBTC [11] methods
8.40 7.94
3.61
29.63 30.11
36.8
7.52 7.13
3.27
31.03 31.52
38.03
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
1 2 3 4 5 6
RMSE,PSNR
1:BTC - gray 2:EDBTC - gray 3:Proposed method - gray
4:BTC - colour 5:EDBTC - colour 6:Proposed method - colour
RMSE GRAY PSNR GRAY RMSE COLOUR PSNR COLOUR

158
transform based compression methods as the BTC can never compete with them because of they
employ a number of different techniques such as discrete cosine transform (DCT)/wavelet transform
(WT), run length encoding (RLE), and Huffman encoding together to perform the task. The proposed
approach may be used to augment performance of existing algorithms. So one can incorporate the
proposed BTC based method to a hybrid image compression method to achieve improved
performance over transform based coding like JPEG.
5. CONCLUSIONS
The proposed colour image data compression scheme using BTC and GA has shown to be
superior to the conventional BTC and EDBTC methods. Several test images, both gray scale and
colour images have been coded with the proposed method, and the resulting reconstructed images are
much better in terms of visual quality. The blocking effect and false contour effect of the traditional
BTC have been removed by using EDBTC and an optimal bit plane obtained with GA. EDBTC is
similar to that of BTC with a few changes and it increases the performance when compared to BTC.
Experimental results have shown improved and acceptable visual quality reconstructed image
obtained using the proposed method with GA when compared to BTC [3] and EDBTC [11] for
grayscale and colour images.
Even though the performance of the proposed method is inferior to the transform coding
methods, the proposed method has a lot of potential with its ability to store edge information. There
are still other aspects, which make the proposed BTC based method a useful alternative for JPEG in
some practical applications. The proposed method is quite simple and easy to implement, probably
much simpler than the JPEG implementation. The encoding phase of the proposed method is fast and
the decoding phase is even faster. Thus the proposed method may be incorporated in a hybrid image
compression technique to achieve an optimal method for colour image compression. More research
would be needed in this direction.
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