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Software development for evaluation and editing raster images
Maria Nazarkevych1, Bogdan Yavourivskiy2, Ivanna Klyuynyk3
1Department
of Information Technology Publishing 2 Automated Control Systems Department, Lviv Polytechnic National
University, S. Bandery Str., 12, Lviv, 79013, UKRAINE, 3 Lviv Technical College, Polytechnic National University
E-mail:nazarkevich@mail.ru
The software evaluates the quality of raster images by
quantitative and qualitative indicators. Quality of image is
formed on the basis of the calculation and correction of
brightness, contrast, tone contrast, luminance contrast and tone
saturation. Experimental results are shown as histograms and
tables. The developed method of convolution modifies color
characteristics of pixels in the selected contour.
Key words – computer science, information technologies,
image.
I. Introduction
Human visual system is the most reliable and perfect
measuring instrument that assesses the quality of the digital
image. Also there is subjective evaluation of raster images,
which is complicated and slow, and requires the
involvement of experienced experts. Therefore, the
development of software for evaluation and editing bitmap
images is an important task and can be used in different
spheres of life.
Figure 1 shows a logical diagram of the software.
Fig.1.Structure of software
There are two approaches to the assessment of
image quality: a quantitative assessment, which is based on
the use of mathematical methods (mean-square error, Lpnorm, measures that take into account the features of the
image the human visual system) and subjective assessment
based on expert estimates [1].
Subjective and quantitative assessment of image
quality can be absolute or comparative. The absolute
measure of quality is used to evaluate a single image, that
image is assigned to the appropriate category in the rating
scale. Comparative measures used for ranking a set of
images for quality scale from "best" to "worst" or mutual
comparison of two images, such as the source and filtered
(or received on different days, different cameras, etc.).
The degree of sharpness S can be determined by
finding the angle of inclination of the profile picture
brightness difference on the border.
To evaluate the image contrast performs
comparing pixels based on specific combinations of image
elements. Moreover, all elements are considered
equivalent. Using the summation rule contrasts, calculate a
set of values which define the perception of each pair of
image elements. The result of averaging matrix of local
contrast is total contrast.
Luminance contrast is the difference between a
natural or apparent brightness of individual sections of the
image. Calculation of physical or apparent brightness can
be viewed as converting a color image to achromatic
colors. Therefore, the luminance contrast - a comparison of
two parts of the image reduced to achromatic colors.
If we analyze the RGB histogram, it can be
concluded that the number of contrasting images of light
and dark pixels should be about the same, the difference in
their brightness - a significant and principal place of
concentration of pixels - near the border of the range.
A good criterion for evaluating luminance
contrast is the variance of luminance pixels.
Index contrast of tone belongs to more complex
indicators of quality. Converted to shades of gray can be
the same brightness, but visually clearly differ.
Tone saturation is difference in color from
achromatic when it is the same brightness. In RGB-cube
tone saturation of the pixel can be expressed as the distance
to the diagonal achromatic colors.
For the evaluation of the entire image tone
saturation can be expressed as the average saturation tone
for all pixels.
Brightness of the image can be expressed as the
average brightness of all pixels (expectation of probability
theory)
Since the sensitivity of human vision to different
parts of the spectrum varies (up in yellow and green, less
red, and even less in blue), brightness of color pixels will
“COMPUTER SCIENCE & INFORMATION TECHNOLOGIES” (CSIT’2014), 18-22 NOVEMBER 2014, LVIV, UKRAINE
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be perceived subjectively, depending on its tonal
characteristics
Convolution - an operation that shows the
"similarity" of a function of the reflected and shifted copy
of the other. Convolution - an operation of calculating the
new value of the selected pixel, taking into account the
value of the surrounding pixels. The form of the used
kernel is determined what kind of action it performs.
Kernel - a coefficient array of fixed size with special
anchor point (anchor), usually located in the center of the
array. The size of the array is called a caliper core.
The value of the convolution at each point is calculated in
the following manner. Put Anchor Point over pixel image,
due to the fact that the rest of the kernel overlaps the
corresponding adjacent pixels in an image. Thus, for each
point of the kernel, we have mentioned the kernel at this
point and also the value of a pixel image that is imposed
under a relevant point nucleus. Then, for each point we
nucleus multiplies these two values and conclude. The
resulting sum divided by the sum of the elements of the
kernel convolution. Then the result is put in the original
image at the position of the relevant provisions of the
nucleus in the input image. This process is repeated for
each point of the image by sliding the kernel over the entire
original image. If we apply the convolution to each pixel
in the image, the result will be some effect, depending on
the chosen kernel convolution. Test results are shown in
the image histogram 2 and Table 1 - Results of numerical
experiments.
Figure 4 shows a histogram of the edited image.
Fig.3. Increasing the brightness using convolution
Fig.4. The histogram of the brightness
Fig. 2. Histograms tests of modified images.
TABLE 1
Image
Sharpness
Brightness
Luminance
contrast
Tone contrast
Tone
saturation
Contrast
Conclusion
1
2
3
4
5
6
7
8
60%
90%
36%
61%
100%
81%
100%
9%
62%
70%
96%
61%
62%
93%
81%
62%
4%
6%
3%
4%
29%
7%
25%
1%
23%
35%
8%
17%
24%
14%
30%
23%
30%
40%
5%
0%
29%
9%
0%
30%
1%
3%
0%
1%
11%
1%
2%
0
Figure 3 shows image with changed contrast and
brightness.
Developed software for evaluation of image quality,
which is based on quantitative and qualitative indicators.
Quality of image is formed on basis of calculation and
correction of brightness, contrast, tone contrast, luminance
contrast and tone saturation. The results of the experiments
are shown as histograms and tables. The developed method
of convolution modifies color characteristics of pixels in
the selected contour.
References
[1] Jain A.K. Fundamentals of Digital Image Processing.
– Prentice-Hall, Inc., USA, 1989.
“COMPUTER SCIENCE & INFORMATION TECHNOLOGIES” (CSIT’2014), 18-22 NOVEMBER 2014, LVIV, UKRAINE
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“COMPUTER SCIENCE & INFORMATION TECHNOLOGIES” (CSIT’2014), 18-22 NOVEMBER 2014, LVIV, UKRAINE