Symbols Frequency based Image Coding for Compression

Symbols Frequency based Image Coding for
Compression
Thafseela Koya Poolakkachalil
PhD Scholar
National Institute of Technology
Durgapur, India
thafseelariyas@hotmail.com
Saravanan Chandran
National Institute of Technology
Durgapur, India
dr.cs1973@gmail.com
Vijayalakshmi K.
Caledonian College of
Engineering, Oman
vijayalakshmi@caledonian.edu.om
Abstract— The main aim of image compression is to represent
the image with minimum number of bits and thus reduce the
size of the image. This paper presents a Symbols Frequency
based Image Coding (SFIC) technique for image compression.
This method utilizes the frequency of occurrence of pixels in an
image. A frequency factor, y is used to merge y pixel values that
are in the same range. In this approach, the pixel values of the
image that are within the frequency factor, y range are clubbed
to the least pixel value in the set. As a result, there is omission of
larger pixel values and hence the total size of the image reduces
and thus results in higher compression ratio. It is noticed that
the selection of the frequency factor, y has a great influence on
the performance of the proposed scheme. However, higher
PSNR values are obtained since the omitted pixels are mapped
to pixels in the similar range. The proposed approach is
analyzed with quantization and without quantization. The
results are analyzed. This proposed new compression model is
compared with Quadtree-segmented AMBTC with Bit Map
Omission. From the experimental analysis it is observed that the
proposed SFIC image compression scheme with both lossless
and lossy techniques outperforms AMBTC-QTBO. Hence, the
proposed new compression model is a better choice for lossless
and lossy compression applications.
Keywords- image compression, lossy compression, lossless
compression, compression ratio, PSNR, MSE, probability.
I. INTRODUCTION
It has been observed that there is an escalation in the use
of digital images for multimedia applications in the recent
years. Over the past few years Internet based applications such
as WhatsApp, Facebook, Twitter, several other social apps,
and websites have been influencing usage of images and
videos enormously. Initially, the focus of the users of these
applications was on chats and text messages. However, there
is a recent shift on the mode of usage of these applications
from chats to sharing information in the form of digital images
and videos. An uncompressed image consumes lot of storage
and internet bandwidth. Hence, compression of digital images
reduces the size of the image and occupies less storage and
consumes less bandwidth. Digital image compression has
been the subject of research for several decades. However,
there has been focus on color image compression research
work due to the immense amount of digital transmission of
images through various internet based applications [1]-[2].
The idea of image compression is to represent the image
with the smallest number of bits while maintaining the
essential information in the image. The compression is
achieved by exploiting the spatial and temporal redundancies.
Advances in wavelet transform and quantization has produced
algorithms that outperform image compression standards. The
three main concepts that sets limit for image compression
technique are image complexity, desired quality, and
computational cost. The visual efficiency of an image
compression technique depends directly on the amount of
visually significant information it retains [3]- [4].
Image compression is classified as lossless image
compression and lossy image compression based on whether
the reproduced image pixels values are same or different
respectively. Several research works has focused on lossy
image compression focusing on Internet based applications
[5]. However, medical image compression schemes prefer
lossless technique, where each pixel value is vital. Other
application of lossless image compression techniques include
professional photography, application of computer vision
analysis on recordings, automotive applications, input for post
processing in digital camera, scientific and artistic images [6]-
[7]. Algorithms used for lossless image compression include
lossless JPEG [8], lossless Joint Photographic Experts Group
(JPEG-LS) [9], LOCO-I [10], CALIC [11], JPEG2000
(lossless mode) [12] and JPEG XR [13]. However, these
algorithms provide a small compression ratio compared with
lossy compression as only information redundancy is removed
while perceptual redundancy remains intact [14]- [15].
Lossy schemes provide much higher compression ratios
than lossless schemes. However, the high image compression
ratio obtained in lossy compression method is at the cost of
the image quality. By lossy compression technique, the
reconstructed image is not identical to the original image, but
equitably close to it [16].
Color image processing is gaining importance due to the
significant use of digital images over the Internet [17]. Since
number of bits required to represent a color is three to four
times more than the representation of grey levels, data
compression plays a central role in the storage and
transmission of color images [18]. It is an important technique
for reducing the communication bandwidth consumption. It is
highly useful in congested network like Internet or wireless
multimedia transmission where there is low bandwidth. There
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
148 https://sites.google.com/site/ijcsis/
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are various techniques for the compression of color images.
In color image compression, the color components are
separated by color transform. Each of the transformed
components is independently compressed [19]. In this
research article, compression ratio of the proposed new
compression model for lossless and lossy compression
techniques is analyzed.
II. RELATED WORKS
Jong et al. proposed a novel coding scheme for block
truncation based on vector quantizer for color image
compression in LCD overdrive. Experimental results showed
that the proposed scheme achieved higher compression ratio
and better visual quality when compared with conventional
methods. This method is suitable for the hardware
implementation in LCD overdrive due to the constant output
bit-rate and the low computational complexity [20].
Banierbah and Khamadja proposed a novel approach that
reduced the inter-band correlation by compensating the
differences between them [21]. The prediction error was
spatially coded by another method. This method had two main
advantages namely: simplicity in implementation and
application over parallel architectures. The comparison of this
technique with various other techniques proved that this
technique is very efficient and can even outperform them. This
technique is applied for lossless, lossy, and scalable coding.
This approach has provided motivation for research on
lossless and lossy compression.
Olivier et al. proposed a coding scheme in two layers. In
the first layer, the image was compressed at low bit rates even
while preserving the overall information and contours. The
second layer encoded local texture in comparison to the initial
partition [22]. This has given the motivation to target for
higher PSNR values while obtaining high compression ratio.
Min-Jen Tsai proposed a compression scheme for color
images based on stack run coding. The highlight of this
scheme was that a small number of symbol set was used to
convert images from the wavelet transform domain to the
compact data structure. The approach provided competitive
PSNR values and high quality images at the same
compression ratio when compared with other techniques [19].
This gave motivation to focus on compression technique
which gives high compression ratio and good PSNR value
with high quality reconstructed image at the same time.
Soo et al. proposed an algorithm to train the color images
based on Kohonen neural network for limited color displays.
It was observed that the Peak Signal-to-Noise Ratio (PSNR)
of the decoded image was high and that a good compression
ratio could be obtained [23]. Experimental results showed that
this method produced an average compression ratio of 13.09
and average Signal-to-Noise Ratio (SNR) of 30.69 [24].This
has motivated to achieve higher compression ratio and SNR.
Chen et al. proposed a color image compression scheme
based on moment-preserving and block truncation coding
where the input image is divided into non-overlapping blocks.
Here, the average compression ratio was achieved 14.00 [24].
This idea inspired to include block truncation during
quantization process of the proposed method.
Panos and Rabab proposed A High-Quality fixed-Length
Compression Scheme for Color Images where the Discrete
Cosine Transform (DCT) of 8x8 picture blocks of an image
was compressed by a fixed-length codewords. Fixed length
encoding scheme was simpler to implement when compared
to variable-length encoding scheme. This scheme was not
susceptible to the error propagation and synchronization
problems that were a part of variable-length coding schemes
[25]. This has shown direction to incorporate DCT based
application of the proposed approach in future work.
Jinlei Zhang et al. propsed a novel coding scheme for the
compression of hyperspectral images. The approach designed
a distributive coding scheme that fulfilled the exclusive
requirements of these images that includes lossless
compression, progressive transmission, and low complexity
onboard processing. Here, the complexity of data
decorrelation was shifted to the decoder side to achieve
lightweight onboard processing after image acquisition. The
experimental results clearly demonstrated that this scheme
achieved high compression ratio for lossless compression of
HS images with a low complexity encoder [26]. This
motivated to focus on research based on low complexity
encoder.
Pascal Peter proposed a new approach where a missing
link between the discrete colorization of Levin et al. [27] and
continuous diffusion-based inpainting in the YCbCr color
space was introduced. With the proposed colorization
technique, it was possible for the high-quality reconstruction
of color data. This motivated to include pixel replacement
technique in this paper based on the frequency of occurrence
of the pixels exploiting the fact that the human eyes are more
sensitive to structural information than color information
[28].
Kim, Han Tai and Kim proposed the salient region
detection via high–dimensional color transform and local
special support which is a novel approach to automatically
detect salient regions in an image. The approach consisted of
the local and global features which complemented each other
in the computation of the saliency map. The first step in the
formation of formation of the saliency map was using a linear
combination of colors in a high-dimensional color space. This
observation is based on the fact that salient regions often
possess distinctive features when compared to backgrounds in
human perception. Human perception is nonlinear. The
authors have shown the composition of accurate saliency map
by finding the optimal linear combination of color coefficients
in the high-dimensional color space. This is performed by
mapping the low-dimensional red, green, and blue color to a
feature vector in a high-dimensional color space. The second
step was to utilize relative location and color contrast between
superpixels as features and then to resolve the saliency
estimation from a trimap via learning based algorithm. This
step further improved the performance of the saliency
estimation. It is observed that the additional local features and
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learning based algorithm complement the global estimation
from the high-dimensional color transform-based algorithm.
The experimental results showed that this approach is
effective in comparison with the previous state-of-art saliency
estimation methods [29]. This motivated to utilize location of
pixels and their pixel values.
Rushi Lan et al. have proposed a local quaternion local
ranking binary pattern (QLRBP) for color images. This
method is different from the traditional descriptors where they
are extracted from each color channel separately or from
vector representations. QLRBP works on the quaternionic
representation (QR) of the color image which encodes a color
pixel using quaternion. QLRBP is able to handle all color
channels directly in the quaternionic domain and include their
dimensions simultaneously. QLRBP uses a reference
quaternion to rank QRs of two color pixels, and performs a
local binary coding on the phase of the transformed result to
generate local descriptors of the color image. Experiments
demonstrate that the QLRBP outperforms several state-of-art
methods [30]. This motivated to club the color channels
together while reconstructing the image.
Yu-Chen Fan et al. proposed a luminance and color
correction scheme for multiview image compression for a 3-
DTV system. A 3-D discrete cosine transform (3-D DCT)
based on cubic memory was proposed for image compression
according to the characteristics of luminance and
chrominance. After designing, the chip could achieve the goal.
This method provided a solution for 3-D multiview
compression and storage research and compression [31].
Seyum and Nam proposed the Hierarchical Prediction and
context Adaptive Coding for Lossless Color Image
Compression where the correlation between the pixels of an
RGB image was removed by a color transform that was
reversible. A conventional lossless image coder was used to
compress luminance channel Y. Hierarchical decomposition
and directional prediction was used for analyzing the pixels in
the chrominance channel. In the later stage, arithmetic coding
was applied to the prediction residuals. From the results, it was
observed that the average bit rate reductions over JPEG2000
for Kodak image set, some medical images, and digital camera
images were 7.105%, 13.55%, and 5.52% respectively [7].
This motivated to split the color channels in the beginning.
Jose et al. proposed logarithmical hoping encoding
algorithm which used Weber-Fechner law to encode the error
between color component predictions and the actual values.
Experimental results showed that this algorithm is suitable for
static images based on adaptive logarithmical quantization [4].
This motivated to do future research work where the error
between the replaced color component and the actual values
can be estimated.
Wu-Lin et al. proposed a novel color image compression
technique based on block truncation where quadtree
segmentation technique was employed to partition the color
image. Experimental results showed that the proposed
approach cuts down the bit rates significantly while keeping
good quality for the reconstructed image [1]. This motivated
to direct the research in a way that high quality images are
obtained at minimum bit rates.
Mohamed et al. proposed a lossless image compression
technique where the prediction step was combined with the
integer wavelet transform. Experimental results showed that
this technique provided higher compression ratios than
competing techniques [14].
Wu-Lin Chen et al. proposed the Quadtree-segmented
AMBTC with Bit Map Omission (AMBTC-QTBO) [1]. In
this approach the color image is decomposed into three
grayscale images. Then each grayscale image is partitioned
into a set of variable-sized blocks using quadtree
segmentation. The algorithm for AMBTC-QTBO is as
follows:
1. Decompose color image into three grayscale images
2. Partition grayscale images to 16x16 non overlapping
block sets
3. Compute the block means and two quantization levels a,
b using AMBTC [32] for each block.
4. If|a − b| ≤ 𝑇𝐻𝑄𝑇, encode 16x16 block using its block
mean and go to 8. Otherwise, divide this block into 8x8
equal-size pixel blocks.
5. Calculate block mean and two level quantization for 8x8
blocks.
6. If|a − b| ≤ 𝑇𝐻𝐵𝑂, encode 8x8 block using its block
mean and go to 8. Otherwise, divide this block into 4x4
equal-size pixel blocks.
7. Calculate block mean and two level quantization for 4x4
blocks. If|a − b| ≤ 𝑇𝐻𝐵𝑂, encode 4x4 block using its
block mean and go to 8. Otherwise, encode 4x4 block by
AMBTC.
8. Go to 3 if there are any blocks to be processed.
In the above algorithm, the predefined threshold THQT is
used to determine whether the grayscale image blocks are to
be further subdivided. Adding to the above, another pre-
defined threshold THBO is used to determine the block
activity for 4x4 image blocks in the bit map omission
technique. In order to perform performance comparison, the
proposed method in this paper has been compared with
AMBTC-QTBO.
The aim of this research article is to propose a novel
compression scheme for image namely, Symbols Frequency
based Image Coding (SFIC) for Color Image Compression.
The following Section III describes the proposed SFIC
compression technique. The following Section IV
Experiments and Results, analyses the proposed SFIC with
lossless and lossy conditions and Quadtree-segmented
AMBTC with Bit Map Omission [1]. Standard images are
used in this experiment. Conclusions are drawn in section V.
III. IMAGE COMPRESSION
Two essential criteria that are used to measure the
performance of a compression scheme are: Compression
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Ratio (CR) and Peak Signal to Noise Ratio (PSNR), which is
the measurement of the quality of the reconstructed image.
a) Compression Ratio
The Compression Ratio (CR) is defined as the ratio
between the size of the original image (n1) and the size of the
compressed image (n2) [33].
𝐶𝑅 = 𝑛1/𝑛2
b) Peak Signal to Noise Ratio
PSNR is an expression for the ratio between the maximum
possible value (power) of a signal and the power of distorting
noise that affects the quality of its representation [34].
The mathematical representation of the PSNR is as follows:
where the MSE (Mean Squared Error) is:
The three basic steps in the compression of still images are
transformation, quantization and encoding. During the
transformation step, the data set is transformed into another
equivalent data set by the mapper. During the quantization
phase, the quantizer reduces the precision of the mapped data
set in agreement with a previously recognized reliability
criterion. In the quantization process, scaling of data set by
quantization factor takes place whereas in thresholding, all
trivial samples are eliminated. These two processes are
responsible for the introduction of data loss and degradation
of quality. The overall number of bits required to represent the
data set is reduced in the encoding phase [2]- [35].
In lossless compression, the quantization step is eliminated
since it introduces quantization errors that inhibit the perfect
reconstruction of the image. The quantization step is usually
used to turn the transformation coefficients from their float
format to an integer format [14]. In the case of lossless
compression, most color transforms are not used due to their
un-invertibility with integer arithmetic. As a result, invertible
version of color transform, the Revertible Color Transform
(RCT) is used in JPEG2000 [12].
In the case of lossy compression, since all trivial samples
are eliminated during quantization, higher compression ratios
are obtained when compared to lossless compression, but at
the cost of poor quality of the reconstructed image.
A. Proposed SFIC Encoding Scheme
In this section, we explain the new compression model
SFIC that utilizes the pixel values that are in the same range.
The block diagram of the SFIC is displayed in following
figure 1.
First, the image is transformed into a matrix. Further, the
R, G, B components (xa(i,j)) of the image is extracted. In
SFIC, pixel values of the R, G, and B components in the
integer format are not converted to YCbCr. This is eliminated
as the reverse conversion from float to integer values is not
taking place. In the next step, the encoding takes place. There
are three stages in the encoder. In the first stage, the pixel
values, hereafter named as symba and their frequency of
occurrence in each component, hereafter named as ncounta is
calculated. symba and ncounta of pixels in each component is
obtained using algorithm 1. In algorithm 1, xa(i,j) , the R,G,B
components of the image is used as the input and seq_vectora,
which is the column wise representation of xa(i,j) is found. In
the next step, the pixel values are sorted and stored in
symba.This is found by extracting the minimum to maximum
values of pixels in seq_vectora. In the next step of algorithm
1, the frequency of occurrence of each pixel in symba is
obtained by histogram analysis and is stored as ncounta. The
overall frequency based coding scheme takes place in
algorithm 2, which is summarized as below.
Fig. 1: Block Diagram of proposed SFIC
In algorithm 2, a frequency factor, y is used to merge y
pixel values. For each of the y symba which are already in
ascending order, the minimum pixel value among the y symba
is made as the new pixel value. The average ncounta of the y
pixels is made as the new ncounta. These steps in algorithm 2
result in the reduction of number of distinct pixels. In the third
stage of the encoder, unique symbols and count are extracted
Algorithm 1 Calculation of symbols and count
if xa(i,j) then
seq_vectora [xa(i,j) (:)]'
symba minimum to maximum of seq_vectora
ncounta histogram bin values of symba
else
exit
end if
(3)
(2)
(1)
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based on the output obtained from algorithm 2. In the next step
compression ratio is calculated.
Algorithm 2 Calculation of frequency symbols and count
if xa(i,j) then
Calculate symba and ncounta by Algorithm 1
zra number of elements in symba
y frequency number
for i 0:y:zra
symba(i+1:i+y) minimum of symba(i+1:i+y)
ncounta (i+1:i+y) average of the of ncounta(i+1:i+y)
end
else
exit
end if
Decoding phase consists of two main stages. In the first
stage, space is allocated for thresholder image based on image
matrix of each component. In the next stage, the decoder loops
over all rows and columns to get pixel value. Here, decoder
checks pixel value of the pixel at a location. If the pixel value
is less than symba, the decoder assign the value of symba as the
new value based on the value of the pixel, otherwise the pixel
value remains unchanged and thus the new thresholder matrix
is formed for each component.
The output of the extracted new thresholder matrix for
each component of the decoder is concatenated to retrieve the
original image and then the PSNR is calculated.
IV. RESULTS
In this section, we discuss the results with standard images.
SFIC is tested with different frequency factor, y and with a set
of test color images shown in table IV (Lena, Peppers,
Airplane, House of size 512 x 512 and Jelly beans of size 256
x 256). Initially a wide range of y values were used. However,
in this research article, the y values that showed abrupt
changes are mentioned. All the test images are in the standard
Tiff format. Here compression ratio and the quality of image
in terms of PSNR are calculated as discussed in section III.
The following table I shows the lossless compression ratio
for the standard images and their average using the proposed
SFIC image compression scheme for different frequency
factor, y.
Here, the results are shown for lossless approach. CR
(14.79) for Lena image is the highest when y = 40, whereas
for Peppers, CR (15.13) is highest when y = 110. In the case
of Airplane, CR (11.57) is at its peak when y = 100. House has
its highest CR (11.83) when y = 20 while Jelly beans has
highest CR (10.24) when y = 50. The analysis work confirms
that the selection of frequency factor plays vital role in
compression. The y has a great influence on the performance
of SFIC. Further, the experiment results shows that SFIC gives
maximum average compression ratio (10.14) when the
frequency factor, y is 20.
Table I Compression Ratio (lossless) of standard images
using proposed SFIC for different frequency factor, y.
Table II PSNR for images using proposed SFIC lossless
image compression scheme for different frequency factor, y.
The above table II shows the PSNR for the standard
images using the proposed SFIC image compression scheme
(lossless) for different frequency factor, y. When y = 10, the
highest PSNR value is 38 for the Airplane image. The least
PSNR value is 25 exhibited by Lena when y = 10. For the
frequency factors (y) 20, 30, 40, 50, 100, or 110, the PSNR
value for Lena is 44 and Jelly beans is 50. This is because the
symbols in the symbol table do not differ much at these values
of y and hence there is no difference between the
reconstructed images for these values of y.
Table III MSE for images using proposed SFIC lossless
Images
y
10 20 30 40 50 100 110
Lena 7.67 10.71 10.89 14.79 7.96 8.63 2.70
Peppers 8.81 9.54 7.65 6.05 6.22 6.57 15.13
Airplane 7.80 10.62 6.55 9.96 10.45 11.57 3.03
House 8.29 11.83 12.24 11.46 8.11 5.46 10.03
Jelly
beans 7.24 7.99 6.87 7.24 10.24 3.69 2.51
Average 7.96 10.14 8.84 9.90 8.60 7.18 6.68
Images
y
10 20 30 40 50 100 110
Lena 25 44 44 44 44 44 44
Peppers 29 ∞ ∞ ∞ ∞ ∞ ∞
Airplane 38 ∞ ∞ ∞ ∞ ∞ ∞
House 29 ∞ ∞ ∞ ∞ ∞ ∞
Jelly beans 37 50 50 50 50 50 50
Images
y
10 20 30 40 50 100 110
Lena 186 2.9 2.9 2.9 2.9 2.9 2.9
Peppers 82 0.0 0.0 0.0 0.0 0.0 0.0
Airplane 10 0.0 0.0 0.0 0.0 0.0 0.0
House 90 0.0 0.0 0.0 0.0 0.0 0.0
Jelly
beans 14 0.6 0.6 0.6 0.6 0.6 0.6
Average 76 0.70 0.70 0.70 0.70 0.70 0.70
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The above table III shows the MSE for the images using
the proposed SFIC image compression scheme (lossless) for
different frequency factor, y. MSE is inversely proportional to
PSNR and this is reflected in table III. When y is 20, 30, 40,
50, 100 and 110 for Peppers, Airplane and House, MSE is 0.
Table IV Test Images and Reconstructed Images with
PSNR(lossless SFIC)
The above table IV shows the test images and
reconstructed Images with PSNR for lossless SFIC. Here,
Lena, Peppers, Airplane, House are of size 512 x 512 and Jelly
beans is of size 256 x 256. Here, the reconstructed images with
peak PSNR is displayed. The same test images is also used for
lossy SFIC. The PSNR value obtained after application of
lossy SFIC is displayed in table VI.
The following table V shows the compression ratio(lossy) for
the standard images and their average using the proposed
SFIC image compression scheme for different frequency
factor, y. The results above are for lossy approach. CR (14.44)
for Lena image is the highest when y = 40, whereas for
Peppers, CR (12.43) is highest when y = 20. In the case of
Airplane, CR (11.37) is at its peak when y = 100. House has
its highest CR (15.04) when y = 30 while Jelly beans has
highest CR (16.97) when y = 40.
Table V Compression Ratio lossy of standard images using
proposed SFIC for different frequency factor, y.
The analysis work confirms that the selection of frequency
factor plays vital role in compression. The y has a great
influence on the performance of SFIC. Further, the experiment
results shows that SFIC gives maximum average compression
ratio (12.44) when the frequency factor, y is 40.
Table VI PSNR (lossy) for images using proposed SFIC
The above table VI shows the PSNR for the standard
images using the proposed SFIC image compression scheme
(lossy) for different frequency factor, y. When y = 10, the
highest PSNR value is 35 for the Airplane and Jelly beans
images. The least PSNR value is 26 exhibited by Lena when
y = 10. For the frequency factors (y) 20, 30, 40, 50, 100, or
110, the PSNR value for Lena is 42, Peppers is 47, Airplane
is 59, House and Jelly beans is 44. This is because the symbols
in the symbol table do not differ much at these values of y and
hence there is no difference between the reconstructed images
for these values of y.
Images
y
10 20 30 40 50 100 110
Lena 7.57 10.53 10.70 14.44 7.86 8.50 2.69
Peppers 8.70 12.43 9.40 7.10 7.32 7.82 2.49
Airplane 7.72 10.45 6.48 9.82 10.29 11.37 3.02
House 8.49 14.44 15.04 13.88 5.69 5.97 11.83
Jelly
beans 8.75 12.07 7.24 16.97 11.08 3.79 2.56
Average 8.25 11.98 9.77 12.44 8.45 7.49 8.82
Name Test Image Reconstructed Image PSNR
Lena
44
Peppers
Infinity
Airplane
Infinity
House
Infinity
Jelly
beans 50
Images
y
10 20 30 40 50 100 110
Lena 26 42 42 42 42 42 42
Peppers 27 47 47 47 47 47 47
Airplane 35 59 59 59 59 59 59
House 28 44 44 44 44 44 44
Jelly beans 35 44 44 44 44 44 44
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Table VII Comparison of Compression Ratio (CR), PSNR
of SFIC and AMBTC-QTBO.
In the above table VII, the proposed image coding scheme
(lossless and lossy technique) is compared with Quadtree-
segmented AMBTC with Bit Map Omission (AMBTC-
QTBO) and the results are analyzed. The average CR using
SFIC in lossless scheme is 10.68, whereas with lossy scheme
is 11.31. This increase in CR is due to the presence of
quantization step in lossy scheme. In the case of AMBTC-
QTBO, the average CR is 5.32. These results indicate that the
CR obtained by proposed lossless and lossy scheme is more
than twice the CR obtained by AMBTC-QTBO. The average
PSNR value with SFIC in lossless scheme is infinity whereas
with SFIC in lossy scheme is 48. The reduction in the PSNR
value in the case of lossy scheme is justifiable as there is loss
of data due to quantization in the reconstructed image. In the
case of AMBTC- QTBO, PSNR is 32. This is far less than the
PSNR obtained by SFIC in both lossless and lossy scheme.
From the analysis it is seen that SFIC image compression
scheme with lossless and lossy techniques outperforms
AMBTC- QTBO.
V. CONCLUSION
In this research article, a novel approach for image
compression based on the probability of the frequency of
occurrence of pixels has been proposed. This method is
compared with the Quadtree-segmented AMBTC with Bit
Map Omission. The average CR with SFIC in lossless scheme
is 10.68, whereas with lossy scheme is 11.31. This increase
in CR is due to the presence of quantization step in lossy
scheme. This means that SFIC with lossless and lossy scheme
provides 50% better color image compression than AMBTC-
QTBO. SFIC in both lossless and lossy schemes also
produced higher PSNR than AMBTC-QTBO showing better
picture quality for the regenerated image. From the analysis
it is seen that SFIC image compression scheme with lossless
and lossy techniques outperforms AMBTC- QTBO. Hence
SFIC is applicable is a better choice for lossless and lossy
applications. For example, SFIC approach with lossless
technique is applicable in medical, scientific, and artistic
images. SFIC approach with lossy technique is applicable for
WhatsApp, Facebook, Twitter and other social media apps
and websites.
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Image SFIC
CR
Lossless
SFIC
CR
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AMBTC
CR
SFIC
PSNR
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SFIC
PSNR
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PSNR
Lena 10.71 14.44 5.99 44 42 33
Peppers 9.54 7.10 6.02 ∞ 47 32
Airplane 10.62 9.82 4.17 ∞ 59 32
House 11.83 13.88 5.10 ∞ 44 30
Average 10.68 11.31 5.32 ∞ 48 32
ISSN 1947-5500

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ISSN 1947-5500

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