Hybrid Algorithm for Enhancing and Increasing Image Compression Based on Image Processing Techniques

Hybrid Algorithm for Enhancing and Increasing Image
Compression Based on Image Processing Techniques
Khalil Ibrahim Mohammad Abuzanouneh
IT Department, College of Computer, Qassim University,
Qassim, Saudi Arabia,
ka.abuzanouneh@qu.edu.sa
Abstract—The data compression and decompression
play a very important role and are necessary to minimize
the storage media and increase the data transmission in
the communication channel, the images quality based on
the evaluating and analyzing different image compression
techniques applying hybrid algorithm is the important
new approach. The paper uses the hybrid technique
applied to images sets for enhancing and increasing image
compression, and also including different advantages such
as minimizing the graphics file size with keeping the image
quality in high level. In this concept, the hybrid image
compression algorithm (HCIA) is used as one integrated
compression system, HCIA has a new technique and
proven itself on the different types of ﬁle images. The
compression effectiveness is affected by the quality of
image sensitive, and the image compression process
involves the identification and removal of redundant
pixels and unnecessary elements of the source image.
The proposed algorithm is a new approach to compute
and present the high image quality to get maximization
compression [1].
In This research can be generated more space
consumption and computation for compression rate
without degrading the quality of the image, the results of
the experiment show that the improvement and accuracy
can be achieved by using hybrid compression algorithm. A
hybrid algorithm has been implemented to compress and
decompress the given images using hybrid techniques in
java package software.
Index Terms—Lossless Based Image Compression,
Redundancy, Compression Technique, Compression
Ratio, Compression Time.
Keywords
Data Compression, Hybrid Image Compression Algorithm,
Image Processing Techniques.
I. INTRODUCTION
Data compression processes are one of the important topics in
the nowadays, and the image processing has become a ground
of it research today. There are different types of data must be
stored in data warehouses, archives, and they must be
transmitted through communication channels, and therefore
several of data compression algorithms were designed for
image processing[2].
The main compression techniques are a lossy and lossless
compression. The lossless compression is applied when the
file information has to be uncompressed as the same it was
before compression. The files can be stored using the lossless
compression technique, losing any data or character could be
made the data misleading the in the worst case. So there are
limits to the amount of space saving that can be gotten with
lossless compression. In general, the ratios of Lossless
compression the range from 20% to 60%, while the lossy
compression process on the image file doesn't have to be
stored completely [3]. Based on the lossless method a lot of
bits can be thrown away from some images, such as audio
data and video when the uncompressed process is done, and
the total of data can be acceptable quality.
Compression image is an encoding rules process for
decreasing the number in the original image to store or
transmit it, the image compression can be identified removed
unnecessary pixels of the source image, by reducing the
memory size that needed for keeping the image high quality.
The lossy compression can be more meaningful when the
compressed images have high quality and in the general can
be satisfactory in the most cases [4].
The goal of the image compression process is to get the
minimum number of bits for storing process and transmission.
In the final experiments, the data encoding could possibly
reach a 30-80% reduction in the size of data.
II. IMAGE PROCESSING AND COMPRESSION
Image processing is the image operation into a collection of
pixels connected together, and it also the most significant task
in the image compression to get better image analysis, the
original image can be formed into different sizes and pieces,
so the most important task in the image compression is to
explore and apply the appropriate algorithms and parameters
selection [5].
Any Image is characterized by a set of pixels, and in the
images, there are a lot of common parts existing with the
neighboring pixels and correlated together include a lot of
redundant pixels. The two supporting components of
compression are irrelevancy reduction and redundancy
reduction.
1. Redundancy Reduction: The property in an image due to
redundant bits is called redundancy. It means the data
duplication in the images.
Data elimination is called redundant data reduction, it helps to
get a minimal storages spaces and results in the image
compression.
The image compression can apply a set of methods for
reducing the total number of bits required for representing an
image achieved by eliminating the difference of redundancy
pixels existing in the pixels of an image[6]. There are three
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basic types of redundancies in the digital image as the
following:
a. Psycho Visual Redundancy is a type of redundancy that
correlates with different sensitivities to whole image
signals using human eyes. So the eliminating process for
some pixels in the image processing is acceptable.
Therefore, the deleting of some bits which less relative
important colors of the human vision can be acceptable
b. Inter-pixel Redundancy is a redundancy corresponding to
statistical dependencies among pixels, especially between
neighboring pixels, the information related to individual
pixels is comparatively small, the neighboring pixels of
image are not independent, due to the correlation between
the images neighboring pixels, the dependencies
between values of pixels in the image is called inter-pixel
redundancy or spatial redundancy [7].
c. Coding- Redundancy: The uncompressed image generally
is coded each pixel in a fixed length, and is implemented
using lookup tables to be reversible in the compression
process. The uncompressed image is coded with each
pixel, Hoffman code technique is the main algorithm to
found coding redundancies.
2. Irrelevancy Reduction– In the image compression can be
applied Irrelevancy technique with the actual information that
is removed to reduce the required bits for compression image
process, the elimination of irrelevant bits will be lost the
information that cannot be recovered. In order to justify this,
the removal of information which is least perceivable by the
human visual system is performed. Irrelevance reduction bits
are used in lossy compression.
The success compression of recognition original image is
related to the edges marked quality.In this research will be
investigated and evaluated edge detection techniques and
active contour model to enhance and detect the image color at
different levels. there are several algorithms are applied such
as Prewitt, segmentation algorithm, and Canny edge detection
based on comparison criteria with artificially generated
images, the edge quality, and map quality are very important
parameters in this stage [8]. The experiments results are
explained that apply to these criteria could be utilized for
further analysis and find the best edge detector in the
compression image.
There are different types of image compression based on
segmentation algorithm are:
1. The regions segmentation compression (RSC) can be used
to cover the image coordinates.
2. The linear structures segmentations compression (LSSC)
that including line segments and curve segments used active
contour model.
3. The two-dimensional shapes segmentations compression
(2DSSC), such as ellipses, circles, and strips (regions, long,
symmetric), the cluster pixels inside salient image boundaries,
the regions corresponding to objects surfaces, or objects
natural parts.
The image segmentation compression (ISC) can be used in
different felids such as the image recognition that is using for
face recognition, the medical image such as diagnosis
operations, locating diseases and other dangers pathologies.
For the video, image systems can be applied ISC in the traffic
control system that is focusing to identify the shapes, sizes,
and moving scene objects. The video image compression is
divided into two approaches of segmentation: the region based
compression, and boundary based compression, in the first
segmentation, the purpose is to determine when a pixel
belongs to an object or not [96], in the second segmentation
the goal is to locate the boundary curves between the
background and the objects.
The region segmentation algorithms can be applied as the
following:
a) The thresholding method technique of region-based
segmentation can be used to segment the original image for
the objects separating from the background using a colors
features values to compare with a threshold values in order to
extract the color pixels class, a method starts from the first
one pixel of a potential region and expands by adding
adjacent pixels for an image that contains different types
regions, the image should be segmented based on the image
different areas which each area has a features values range,
the thresholds are important to select a colors features values
of the image regions to be very useful and effective in the
images segmentation quality and compression process [10] ,
after this stage, the statistical test can be used to take a set of
decisions related to which the pixels can be deleted into a
region segmentation or not for increasing the image
compression ratio.
b) Clustering-based colors segmentation technique
Any image can be divided into different classes or the same
type of classes, the redundancy pixels of the image colors
should be collected together in the similar classes for building
the compression algorithm, and the different colors that
contain a different type of pixels will be in different classes.
c) Edge-based colors segmentation technique is the main
features technique of the colors image, which includes
valuable pixels in the image analysis and diagnosis
classification to explore the boundaries detection between the
various region's colors by using the selected features of the
pixels values as textures, and intensities of the image colors.
III. LOSSLESS METHOD OF IMAGE
COMPRESSION
Lossless methods are usually having two stages of algorithms
operations. The first stage is transforming the source image
into another format for reducing the redundancy colors. In the
second stage can be used an entropy encoder for removing the
coding redundancy. The lossless decompressors are strong
inverse processes of the lossless compressors [11].
In the medical images can be used lossless compressors
methods to get more than 50 % of original image size.
While can be applied entropy methods in the lossless
compression with several an application to compute MSE
(mean square error ) and PSNR (peak signal to noise ratio )
between images and digitized radiographs, X-rays, and
gamma rays to found a bit rate from 4 to 5 bpp (bytes/pixel).
In the Lossless compression can be applied various methods
such as linear transformation, multiresolution methods, and
investigated prediction in the decorrelation medical images
before the coding stage, and getting best results.
The compression results were 3:1 for angiograms images, a
less than 2:1 for MRI images.
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The interpolation techniques and found-linear prediction
interpolation techniques can be given good results in the
compression ratios [12].
IV. IMAGE SEGMENTATION USING
HICA ALGORITHM
The parameters selection and different types of algorithms
will be applied to enhance the images segmentation and
improve outputs file. The pixel scale and level of
segmentation implement based on HICA algorithm to
complete region labeling tasks of the image segmentation
processes, the HICA algorithm should be used the image
adaptive segmentation included the following steps [11]:
1. Compute the image statistics tables give us the probability
for a given degree of a confidence level and identically
distributed normally colors to select suitable threshold.
2. The segmentation process will generate initial values for
image pixels.
3. Compute the segmentation of image pixels based on
quality measures to satisfy conditions of the segmentation
function.
4. The image segment should be used new parameters to
calculate and keep the image segmentation quality.
5. The analyzing and modifying process based on the
knowledge structures of the new image will be implemented
by calculating MSE and PSNR for each image.
V. IMAGE COMPRESSION AND CHROMATIC
FEATURES
In the research paper will apply HICA algorithm using
chromatic features to determine and describe RGB colors
distribution and the grey-level of an image, which are the
most discriminative features of the image compression. the
image pixels are represented a segmented object, The
selection parameters are used to detect the edge of image
boundaries that have the same colors of pixels from the
current image that will be used extracted chromatics features.
The convergence process can be completed and achieved in
under the number iterations required to detect the chromatic
features and complete colors counted for the image
compression.In the next step, the solutions have represented
the intensity of colors pixels and chromatic features which
can be detected and computed using the Hybrid algorithm of
image compression. In this stage of research will improve its
searching capacity for the image process environment. The
image process is a stochastic process where pixels values are
modeled as random variables, for calculating the probability
density of grey level and color distribution as its image
compression [13].
In this stage of image processing can get robust convergence
as building simulations for image compression as possible
with the reliable and high convergence of the original image.
the compression hybrid algorithm efficiently improves the
performance compression in the image processing
environment, and the best selection individual of color pixels
based on features function to finding probability density of
grey level, colors gradient, colors distribution, pixels color
and boundaries shapes into the original image. The selection
operators of HCA will be selected set of colors pixels to be
the best solutions that have a better classification, based on a
features function for reconstruction new image [14].
VI. PROPOSED SYSTEM
The proposed analysis system (HICA) of the image
compression process will be explained the phases are shown
in figure 1. The Transform process is applied for improving
contrast variation and luminance in the original images. In the
second phase, segmentation processes are applied and
implemented to explore and isolate the interest pixels colors
and remove noises before the image compression. The third
phase goal is to extract the image characters to be used in the
next phase of the compression process; the Features selection
method is applied to decrease the redundant pixels and built a
classification of the new Image. The selected features are
selected for input to the classification method and take the
decision about the class assignment by using the hybrid
algorithm as shown in figure 1, the goal of the segmentation
process in image compression is to separate colors from
another different ingredient of the image[15].
Fig 1: Illustrates the block diagram of the HICAs proposed a
system based on Lossless image compression.
VII. ACTIVE CONTOUR AND IMAGE
SEGMENTATION MODELS
In the last years, there are recent developments in the image
compression fields with a new techniques research for
improving features analysis of image compression images. the
techniques have been developed to identify specific structures
in image colors. The active contour one of the main methods
can be adaptable for the required and desired features in the
image compression, an active contour is using to delineate an
object outline of a possibly noisy for 2D images, and can be
applied in several fields such as shape recognition, object
tracking, edge detection and segmentation image[15].
In this research, experiments have a set of forms and several
types of images. The selection process of an appropriate
method for image variable colors and segmenting for specific
type image have been always and forever a challenge to select
the algorithm of the image compression. In the active contour
method there are a lot of enhancements and implemented in
Image Decompression
Inverse Transform
Transform Image
Image Segmentation
Entropy Coding
Image Edge Detection
Image Feature Extraction
Original Image
Image compression
Entropy Decoding
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the image colors segmentation, in some images should be
applied active contour method in the changeable curves and
change forms to avoid distort colors of image boundaries in
the segmentation process[16].
The active contour models can be moved based on internal or
external forces extracted based on different characteristics of
the image colors. The active contour adaptation occurs in
response to both internal and external forces, the external
forces model has described the grayscale level gradient, the
active contour models can be divided into two types: the
parametric models like the Snakes model, which defines a
flexible contour that can dynamically adapt to required edges
of the image colors, and the geometric models, such as the
Level Set model it embeds the front to be zero level set in
the higher dimensional function, to calculate the new function
evolution, this evolution operation is dependent on the image
characteristics extracted and geometric restrictions of the
function, the segmentations of image colors are implemented
on sub-images, the parametric active contour model is a curve
x(s) defined in Equation.1 [17], to be moved in the image
spatial domain to minimize the time and energy function
Emin(t,s) is defined in Eq.1, and therefore the ccompression
time will be decreased as possible is defined by Eq.1.
( )
= ( ) , ( ) , ∈ {1,0} (1)
( ) = ( | ( )| ) + (| ( ) | ) + ( ) (2)
Where x'(s), x″(s) use to found the first and second derivative,
of x(s) respectively. α, β donate the weighting parameters of
active contour model. Ext is the function of external energy
which is derived from the image to take smaller values of
boundaries features [18].
Energy Surface and Optimum Thresholding is the basics
approach to image segmentation is an amplitude thresholding,
a threshold T is chosen to separate the two regions modes, the
image point for I(x,y) >T is considered as object points[19],
otherwise, the point is called a background point. The
threshold method is defined by Eq.3.
( , ) =
1 , ( , ) ≥
0 , ( , ) <
(3)
Where T is set on the entire image basis I(x, y), and the
threshold is global. When T depends on spatial coordinates x
and y, based on a dynamic threshold, when T depends on both
I(x,y) and set property p(x,y) of local image[19], the average
of gray level in a neighborhood centered on I(x,y), the
threshold will be local and T is set according to a fitness
function is defined by Eq.4. :
( , ) = ( , ), ( , ) (4)
The object locating will be described in the image I[x,y],
using a template T[x,y], The best match Searching to
minimize the mean squared errors (MSE) is written below:
, = , − ( − ), ( − ) ( )
VIII. COMPUTE MSE, PSNR,
AND ENTROPY
A. Mean Square Error (MSE)
In this part of the research, the MSE will be computed MSE
between the source image and the compressed image. The
MSE lower values mean minimum error as seen in the inverse
relation among the PSNR and MSE.To find the PSNR, in the
first, it should calculate the mean-squared error is defined as.
=
| ( , ) − ( , )|
.,
(6)
In this equation, x, y denote the rows number and columns in
the original images, respectively.
B. Peak Signal-to-Noise Ratio (PSNR)
The PSNR block computes PSNR between two images. The
ratio will be used as a quality measurement of the source
image and a compressed image. PSNR will be used as a
quality measure of the image reconstruction, the higher PSNR
should be evaluated the higher quality reconstruction.
The PSNR is computed as in the following equation:
= 10 (7)
In the above equation, where R denotes the maximum range
in the data type of original image. For example, the data type
has an 8-bit integer of the image, so R = 255.
C. Image Entropy
Entropy image encoding is a lossless compression that can be
implemented on an image color after the quantization process
to represent image colors in a more efficient with minimum
memory for saving stage or transmission. In the research
paper will be applied the entropy encoding to increase the
image compression ratio.
In this paper will use the entropy encoding to evaluate and
describe the image quantity, the pixels amount that should be
coded by a compression algorithm. The low entropy images
which containing a black sky, and has little bit contrast with
pixels large run with similar digital numbers. In the image is
perfectly flat, the entropy will equal zero. So, the image can
be compressed to a small size.
In the terrain images that have a lot of difference in a contrast
from one pixel to another, it has very high entropy and cannot
be compressed as much as the low entropy of image.
In the image, colors have corresponded to the gray levels and
the individual pixels can be adopted. In the image pixels that
have been perfectly histogramming equalized, the pixels
should be equally occupied in all states of pixels, and the
spread of pixels is a maximum for the image entropy. On the
other hand, the image colors which have been a threshold,
there are two states are occupied and the entropy will be very
low.
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The image entropy is equal zero when the pixels have the
same values. in this research progression, we can note that the
image entropy is decreased, in this case, the image colors
should be moved from a full grayscale image to a threshold
binary image (high entropy to zero entropy). So the
compression ratio will be increased.
All changes in the image colors are meaningful, the Changes
of image pixels that due to noise to be part of an image, and it
represents the image as more than required. the heat and
Noise can be played the same roles in the entropy increased of
Images. The image entropy H is defined as.
= − ( ) (8)
Where M is equal the gray levels number, while pi is the
correlated probability with gray level i.
Maximum entropy is achieved in the case of a uniform
probability distribution, where M=2n and Pi is constant, given
by
= −
1
2
(9)
The maximum entropy is found from
=
1 1
(10)
The image minimum entropy will be achieved if the image is
not a variable, and all pixels have identical gray level i. For
where the gray level pi = 1, and H = - log (1) = 0. The image
entropy can restrict the lower bound on the bits average
number per pixel for an image encoding without distortion
and can apply to uncorrelated images is shown in figure 2.
Fig 2. Illustrates a two image, the right has random noise; the
right image has the same gray levels distribution.
The left image has random noise and its entropy contains 8
bits and is uncompressible. The right image has the same gray
levels distribution with strongly spatially correlated.
IX. EXPERIMENTS AND RESULTS
The proposed algorithm is applied to various types of Images
such as medical images, classical images, commercial digital
camera images etc. The experiments results show that the
proposed algorithm has better efficiency and activity in the
images compression from other algorithms; the results of the
experiment were implemented in the Java application for
image compression algorithms Package as illustrated above
[1].
In the first experiment of the HICA proposed, the results
quality and the compression ratio among the compressed
image and the uncompressed image should be taken into
consideration.
The final size of compressed images is compared with the
original images between Huffman Algorithm and HICA.
Also, the compression time increases as the original size
increases, and in contrast, the compression ratio decreases as
the original file size increases. The algorithm gives a good
compression ratio that lies between 30% and 58%. The results
experiments are explained in table 1.
TABLE1.
Shows results analysis between Huffman Algorithm and HICA
for compression images and space saving.
Images
FileSize
(bits)
Huffman Algorithm HCIAs
Compression
Size(bits)
SpaceSaving
(Huffman)%
Compression
Size(bits)
SpaceSaving
(HCIAs)%
ImgA 37492 26591 0.29 24174 0.36
ImgB 27819 12716 0.54 11560 0.58
ImgC 32591 23645 0.27 21495 0.34
ImgD 24088 17409 0.28 15826 0.34
ImgE 16564 11055 0.33 10050 0.39
ImgF 13190 8885 0.33 8077 0.39
ImgG 31902 22909 0.28 20826 0.35
ImgH 22081 15040 0.32 13673 0.38
ImgI 42365 32124 0.24 29204 0.31
ImgJ 28177 18806 0.33 17096 0.39
ImgK 9070 6095 0.33 5541 0.39
ImgL 23524 18592 0.21 16902 0.28
ImgM 20183 13108 0.35 11916 0.41
ImgN 27138 19693 0.27 17903 0.34
ImgO 50885 26701 0.48 24274 0.52
ImgP 20033 14062 0.30 12784 0.36
ImgQ 22384 15728 0.30 14298 0.36
In the second experiment, we compared the results analysis of
the space saving and compression images between LZW
Algorithm and HICA. The results obtained after comparing
and it was in an acceptable range, we can observe that the
HICAs algorithm performs in an efficient way and gives
better results is shown in table 2.
TABLE 2.
Shows results experiments between LZW Algorithm and HICA
of space saving for compression images is shown in table 2.
No FileSize
LZW Algorithm HCIAs
Compressio
n
Size(bits)
SpaceSavin
g (LZW)%
Compressio
n Size(bits)
SpaceSavin
g
(HCIAs)%
ImgA 37492 29009 0.23 24174 0.36
ImgB 27819 13872 0.50 11560 0.58
ImgC 32591 25794 0.21 21495 0.34
ImgD 24088 18991 0.21 15826 0.34
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ImgE 16564 12060 0.27 10050 0.39
ImgF 13190 9692 0.27 8077 0.39
ImgG 31902 24991 0.22 20826 0.35
ImgH 22081 16408 0.26 13673 0.38
ImgI 42365 35045 0.17 29204 0.31
ImgJ 28177 20515 0.27 17096 0.39
ImgK 9070 6649 0.27 5541 0.39
ImgL 23524 20282 0.14 16902 0.28
ImgM 20183 14299 0.29 11916 0.41
ImgN 27138 21484 0.21 17903 0.34
ImgO 50885 29129 0.43 24274 0.52
ImgP 20033 15341 0.23 12784 0.36
ImgQ 22384 17158 0.23 14298 0.36
In the third experiment, we calculated and estimated the space
saving for HICA, LZW, and Huffman algorithm side by side
the results have been increased estimated the space saving and
getting better space saving after improving and minimizing
the values of the image pixels in the transform image stage
using HICA, the probability increment of an image pixels
gives more flexibility and increases the code word and the
space saving. The experiment results were satisfied and get
good results is illustrated in Figure 3.
Fig 3. Illustrates a set of experiment analysis for comparing
and estimating the space saving for HICA, LZW, and
Huffman algorithm in the different types images and sizes.
In the fourth experiment, we calculated and compared the data
compression ratio between the uncompressed and compressed
of different files size. The compression ratio values were
between 1.39 to 2.41. The values obtained after comparing are
in an acceptable range. The experiment of the compression
ratio was satisfied the results are illustrated in Table 3.
TABLE 3.
Shows results experiments of the data compression ratio
between different files size for HICA algorithm is shown in
table 3.
Hybrid Image Compression Algorithms (HICAs)
FileSize CompSize CompRatio CompTime DecompTime
23524 16902 1.39 2.8 3.20
42365 29204 1.45 2.9 3.34
27138 17903 1.52 3.0 3.49
32591 21495 1.52 3.0 3.49
24088 15826 1.52 3.0 3.50
31902 20826 1.53 3.1 3.52
37492 24174 1.55 3.1 3.57
22384 14298 1.57 3.1 3.60
20033 12784 1.57 3.1 3.60
22081 13673 1.61 3.2 3.71
13190 8077 1.63 3.3 3.76
9070 5541 1.64 3.3 3.76
16564 10050 1.65 3.3 3.79
28177 17096 1.65 3.3 3.79
20183 11916 1.69 3.4 3.90
50885 24274 2.10 4.2 4.82
27819 11560 2.41 4.8 5.53
In the fourth experiment, we compared between HICA, LZW,
and Huffman algorithm after calculating the compression ratio
in the different sizes of images files, there was effectiveness
in this experiments of HICA algorithm, The values obtained
after comparing are in an acceptable range, the results analysis
is in an acceptable range.
Fig 4. Illustrates a set of experiment analysis for comparing
and estimating the space saving for HICAs, LZW, and
Huffman algorithm in the different types images and sizes.
In the fifth experiment, we calculated the compression times
for HICAs, we applied Feature Extraction method to enhance
time ratio in the image compression, the results experiment of
CPU time utilized in a compression and decompression was
satisfied and in an acceptable range from 2.8 to 4.8 is
illustrated in Figure.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
23524
42365
27138
32591
24088
31902
37492
22384
20033
22081
13190
9070
16564
28177
20183
50885
27819
SpaseSaving (LZW) SpaseSaving (Huff)
SpaseSaving (HCIAs)
1.00
1.50
2.00
2.50
23524
42365
27138
32591
24088
31902
37492
22384
20033
22081
13190
9070
16564
28177
20183
50885
27819
CompRatio(LZW) CompRatio(Huff) CompRatio(HCIAs)
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Figure 5. The comparison image based on CPU Time utilized
using feature extraction method to enhance time ratio in the
compression and decompression of the image colors.
In the sixth experiment, the compression ratio will be
calculated to define the total of bits number to extract size of
the original image, and compared to the bits number to
represent the compressed image size, and display how much
time an image can be compressed, and determines the
distortion process in the image will be compared with the
source image . The matrices quality measurement can be
implemented used the following tools measures is illustrated
in Table 4.
TABLE 4.
Shows the results analysis are calculated using the quality
measurement matrices of the MSE, PSNR, and Entropy
method.
No MSE PSNR Entropy
Image1 19.594 35.210 7.915
Image2 39.188 32.199 7.437
Image3 58.782 30.438 7.765
Image4 78.376 29.189 5.727
Image5 97.970 28.220 7.680
Image6 117.564 27.428 6.621
Image7 137.158 26.759 7.449
Image8 156.752 26.179 6.928
Image9 176.346 25.667 7.610
Image10 195.940 25.210 7.751
Image11 215.533 24.796 7.549
Image12 235.127 24.418 6.026
Image13 254.721 24.070 6.884
Image14 274.315 23.748 7.246
Image15 293.909 23.449 6.915
Image16 313.503 23.168 7.611
Image17 333.097 22.905 4.404
In the sixth experiment, the PSNR will be used as a measure
of the quality of the image reconstruction. In the higher
PSNR
Should be indicated the reconstruction of a higher quality of
image colors is illustrated in Figure 6.
Figure. Illustrates a set of experiment analysis of the Peak
Signal To Noise Ratio in the different types and sizes of
images.
In the seventh experiment, we calculated the MSE of
cumulative-squared error between the compressed image and
the original source as shown in figure 7.
The MSE lower values mean minimum error as seen in the
inverse relation among the PSNR and MSE as illustrated in
figure 7.
Figure 7. Illustrates a set of experiment analysis of Mean
Square Error in the different types and sizes of images.
In the eighth experiment, the final results experiments of the
Entropy was satisfied the probability of the image pixels
after using active contour model to minimize energy function
for image edge detection and boundaries, the entropy method
in compression image, can be determined the total of pixels
are actually present in the image colors, and the symbol
entropy is the negative logarithm and its probability.
The entropy result is illustrated in Table 8.
Figure 8. Illustrates a set of experiment analysis of Entropy
method in the different types and sizes of images.
0.00
2.00
4.00
6.00
23524
42365
27138
32591
24088
31902
37492
22384
20033
22081
13190
9070
16564
28177
20183
50885
27819
Compression ratio Compression time Decompression time
0
10
20
30
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
PSNR
0
100
200
300
400
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
MSE
0.000
2.000
4.000
6.000
8.000
Entropy Method
Entropy
Vol. 16, No. 3, March 2018
ISSN 1947-5500

X. CONCLUSION AND FUTURE
WORK
In this research, .we applied Hybrid image compression
algorithms on different images. The results of an experiment
for CPU time utilized in a compression and decompression
was satisfied and an acceptable.
We developed Hybrid image compression algorithms to be
capable of extending the image compression based on image
processing techniques for better space saving.
we recommend using this approach of Hybrid image
compression algorithms (HICA) for enhancing and increasing
compression images.
In the Future work will apply the multi-objective of genetic
algorithm based on image techniques with different types of
optimization to improve the performance of Image
compression in all entire domains.
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[2] Khalil Ibrahim Mohammad Abuzanouneh.,” Develop
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[18] Canny J.”A computational approach to edge detection.”,
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Version from: 18.6.2010.
Dr. Khalil Ibrahim Mohammed Abuzanouneh is an assistant
professor in Computer College at the Department of
Information Technology, Qassim University. In teaching, he
has been focusing on applying genetic algorithms and solves
the problem based on genetics approaches in Computer
Education. In research, his current interests include Image
processing, optimization problem, data compression and data
security. Dr. Khalil received his Ph.D. degree in Computer
Engineering, “Computer Aided Design”.
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ISSN 1947-5500

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