International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 3- June 2015
Quality Measure of Multicamera Image for Geometric Distortion
Mahesh G. Chinchole1, Prof. Sanjeev.N.Jain2
M.E. IIndYear student1, Professor2, Department of Electronics Engineering, SSVPSBSD College of Engineering, Dhule
North Maharashtra University, Jalgaon, Maharashtra, India
Abstract — To create the multiple events into the single image is a
instead of reference [5]. As per the availability perfect
reference image that is supposed to have ideal image quality,
the image quality measures can be classified into full
reference (FR), reduced reference (RR), and no-reference (NR)
methods. In FR measure, the full access to the reference
image is available, while in NR no access to the reference
image. The RR, the partial access is available to the reference
image. The access means amount of information or features
extracted from the reference image is available for the
assessment of the quality of the distorted image [6]. To
measure the quality of the distorted image, FR methods [7]
provide the most accurate results in contrast with RR & NR.
The conventional Full reference image quality assessment
methods calculates peak –signal-to-ratio (PSNR) and Mean
Square Error (MSE) i.e. the pixel-wise distances between a
distorted image and reference image [8]. The digital images
are subject to a variety of distortions that may result in
degradation of visual quality throughout acquisition,
processing, compression, storage, transmission and
reproduction. So, it is essential for numerous applications to
Keywords— PSNR, MSSIM, VIF, MIQM, Image quality assessment, be able to measure the image quality degradation that occurs
in a system [5].
Full reference, reduced reference, No reference.
The objective of this paper is to introduce the objective
quality measure for geometrically distorted multicamera
I. INTRODUCTION
The image quality can be measured interms of either images, which has the better connection with human
subjectively or objectively [1] [2] [3]. The objective image observation of the distortions. The basic idea of our approach
quality measure means that the image quality is measured is that the human vision is extremely perceptive to changes in
either by some methods, techniques or by algorithm. In structures in images, thus structural distortion should be a
contrast, in subjective image quality measure the peoples are good estimation of the supposed image distortion. In full
asked for their opinion on the image quality because in most reference approach we have the reference image. Then for
cases human eyes are the decisive receivers. The mean multicamera image quality measure we create the
opinion score (MOS) [4], provides a mathematical analysis of geometrically distorted image. After that we compare both the
the apparent quality of an image and is obtained from a images by various quality measure techniques. In the second
number of human being observer. This method has been used comparison we use the Barbara standard images which are
from many years; but, the MOS is monotonous, quite local geometric distortions applied from [5]. Then we
expensive in terms of time and human resources. Additionally, performed the analysis which shows the success of our
the subjective quality measures results depend on numerous approach in contrast to others.
external factors for example the observer’s surroundings,
II. GEOMETRIC DISTORTION IN MULTI
interest motivation, etc [5]. So it shows that the manipulation
CAMERA IMAGE SYSTEM
is more possible in case of subjective measure. In objective
The
geometric
distortion can be defined as the shifting of
image quality measure, the distorted image is compare with
the reference image. In a simple way the, the analysis is done pixels or overlapping of pixels on each other in the image. For
by subtracting the reference image from the distorted image a multicamera image the particular scene is to captured by
which leads to the mean square error (MSE) or Peak signal to number of cameras from different positions or by different
noise ratio (PSNR) [1][2][3]. The aim of objective image angles. The example of such system is shown in figure.1.
quality measure is to predict the perceived image quality by
human vision model which can predict the perceived image
quality without human intervention. The objective measure
should provide the mathematical value to the persons having
dissatisfaction when they observe the reproduced image
simple way to look all the events in a single look. For this multicamera images have to combine into single image also known as
multi-view image. When we combine the multiple images into single
image, due to misalignment and different camera orientation as well
as arrangement the geometric distortions comes into picture. The
proportional distortion variation between two separate camera
images is the main aspect while measuring the required quality of the
final image. So the quality measure or quality analysis is the most
important (step) or factor in multicamera images. There are several
objective & subjective methods have been proposed for single
camera images but no such comparable efforts has been taken on
multicamera image quality measure. This paper details the methods
and results of implementing MIQA multicamera image quality
analysis. Here we show the methods like PSNR, MSSIM, and VIF for
the measurement of quality of multicamera image and then compare
their results with MIQM. The experimental analysis shows the
effectiveness of the every method in comparison with others. Here we
consider the 1 value for original or reference image and 0 values for
complete distorted image. The range of MIQM is ranging from 1 to
0
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 3- June 2015
a) Parallel.
b) Convergent
c) Divergent
Fig. 1 Three possible camera configurations, i.e., parallel view, convergent
and divergent view.
The fig. 1 shows the three different cameras configurations
which are placed to capture the event. As due to different
camera positions with different angles in short with different
orientations with camera calibration parameters [9] the
geometric distortions will be created in creating the
multicamera images. The Geometric distortion can be divided
in two ways as linear geometric distortion and angular
geometric distortion. The linear geometric distortion occurs
during the rotation, translation, in motion. In this position of
the pixels gets shifted or overlapping of pixels is happened.
The angular geometric distortion occurs during the mapping
like 3-D plane to 2-D plane. The examples of linear and
angular geometric distortion are given below [10] [11].
(a) Original
(c) Linear Distortion.
(b) Original
(d) Angular Distortion (rotation).
Fig.2. Example of geometric distortion in multicamera images.
Fig.2 shows the examples that demonstrate the types of
geometric distortion with original image. The image in
fig.2.(d) is cause to undergo the angular (rotational) distortion.
The columns look closer compare to original image shown in
fig. (b) .Image in fig. (d) rotated by angle 3 degree clockwise.
In multicamera system such distortion can occur when there is
mapping of certain camera plane to reference camera plane.
In single-view images the geometric distortions have been
considered in [12]. The authors proposed the complex wavelet
domain image comparison which is unresponsive to spatial
translations. The proposed model considered that the single
ISSN: 2231-5381
view image is insignificant due to perceptual distortions
caused by spatial scaling, rotation, and translation. Though,
this consideration is not true for multiview images, where
discontinuities, misalignments, blur, and double imaging can
effect in catastrophic distortions. Thus, a precise multicamera
image quality assessment must report for geometric
distortions [12] [9].
I.
QUALITY ASSESSMENT OF MULTICAMERA
IMAGES
The image analysis is concern with the extraction of
measurement, data or information from an image by automatic
or semiautomatic methods. The image analysis is
distinguished from other types of image processing such as
coding, restoration, and enhancement. In image analysis, the
ultimate output is usually numerical output rather than picture
or image. [11][13]. The techniques used for extracting
information from an image are known as image analysis
techniques or image quality measurement techniques. An
image composed of edges and shades of gray. Edge is
corresponding to fast change in gray level and thus
corresponds to high frequency information. Shade is
corresponds to low frequency information. Separation
(filtering) of high frequency information means edge detection.
An edge or boundary is the external information of image.
The internal features in an image can be found using
segmentation and texture. These features depend on the
reflectivity property. Segmentation of an image means
separating certain features in the image. While treating other
part as a backdrop if the image consists of a number of
features of interest then we can segment them separately.
Texture of an image is quantitatively described by its
roughness. The roughness index is related to the spatial
repetition period of the local structure. It is necessary to
segment the image based upon uniform texture before its
measurement. Image feature is a distinguishing characteristic
of an image. Spectral and spatial domain is the main methods
used for feature separation Motion of an object studied from
study of multiple images, separated by varying periods of
time[11] [14].
There are several applications of multicamera system and
every application has precise meaning for post processing and
presentation [15]. In these a single camera is generally chosen
as reference camera for estimating plane or geometry [16].
Here we are presenting the full reference system and
estimating the image quality for multicamera system. To
simulate distortions in multicamera images, a single digital
camera was used to capture high-resolution images. Each
image was then divided into various sub-images. Then these
images are then combining with some % of overlap. The
overlap areas where different with each image; yet, they were
all in the range of 5%, 20% & 40% of the original image.
After that the quality measurement of the distorted image is
done by different quality methods like PSNR, VIF, MSSIM
and finally with our approach MIQM.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 3- June 2015
I.
PROPOSED SYSTEM ARCHITECTURE
As we know the multicamera images are suffers from mainly
two types of distortion Geometric distortion and Photometric
Distortion. So we obtained the results by considering the
individual distortions. Here we simulate the single camera
images for the geometric distortion in case of Multiview.
B) LINEAR GEOMETRIC DISTORTION - To simulate
the geometric linear distortion in multicamera Image system,
the geometric distortions were applied to the images shown in
fig. 3. In this we Split image into two parts (Left and Right).
Here we created the 3 different images having different
amount of overlap. The amount of overlap should be a design
parameter. In the first image we overlap the two parts by 5%
and it is named as low overlap image and then calculated the
PSNR, VIF, MSSIM and finally the MIQM. In the second
image we overlap the two parts by 20% and it is named as
medium overlap image and then calculated the PSNR, VIF,
MSSIM and finally the MIQM. In the third image we overlap
the two parts by 40% and it is named as high overlap image
and then calculated the PSNR, VIF, MSSIM and finally the
MIQM.
Fig.3 shows the three examples of geometric distortion in
multiview images. Fig. 3 (a) is an original image.
(a) Original Image
PSNR = 1,
VIF = 1,
MSSIM = 1, MIQM= 1
(b) Low Overlap (5 % overlap)
PSNR = 0.3422, VIF = 0.9857,
MSSIM = 0.8719, MIQM= 0.6573
(c) Medium Overlap (20 % overlap)
PSNR = 0.3196, VIF = 0.9501,
MSSIM = 0.6927, MIQM= 0.4059
(d) High Overlap (40 % overlap)
PSNR = 0.3037, VIF = 0.9083,
MSSIM = 0.4893, MIQM= 0.1970
II. EXPERIMENTAL RESULT
The objective of MIQM is to obtain an innovative quality
measurement for multi-camera images. As the quality of
images are affected by many factors like number of cameras,
camera configuration, calibration process, quality evaluation
for such an image should take all these into consideration. To
design an objective metric for multi-camera images, the visual
distortion is identified into two types, photometric distortion
and geometric distortion, which can be translated into
luminance, contrast, spatial motion and edge-based structure
components. The main thought of the paper is to first measure
each component by proposing different index values and then
to combine those indexes into one quality, MIQM, to capture
the perceptual quality of multiview images. MIQM shows the
measurement that is full -reference and designed to evaluate
multi-view images, where the reference is regarded as the set
of images taken by identical cameras. Different types of
distortions can be calculated in MIQM according to the
following method describe below.
A) MIQM RESULTS: The results of MIQM are obtained
by following steps.
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Fig.3. Linear Geometric distortion. (a) Original Image (b) Linearly overlaps
by 5% (c) Linearly overlaps by 20% (d) Linearly overlaps by 40%
Images
Quality
Methods
Original
Image
5%
Overlap
Image
20 %
Overlap
Image
40 %
Overlap
Image
PSNR
1
0.3422
0.3196
0.3037
VIF
1
0.9857
0.9501
0.9083
MSSIM
1
0.8719
0.6927
0.4893
MIQM
1
0.6573
0.4059
0.1970
Table.1. Multicamera Image Quality Measure Methods Results for the
geometrically distorted Multicamera Images
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 3- June 2015
(a)
Original Image
(a) Staircase Plot
(b)
Medium Distorted Image
(b) Scatter Plot
(c) High Distorted Image
Fig.5 Geometrically distorted standard Barbara Images. (a) Original Image
(b) Medium Distorted Image, (c) High Distorted Image.
Images
(c) Bar Graph
Fig.4. Image Quality Measure results in the form of (a) Staircase Plot, (b)
Scatter Plot, (c) Bar Graph
Quality
Methods
Fig. 4 shows the quality measurement for multicamera images
with the methods like PSNR, VIF, MSSIM, MIQM interms of
graph & plots like Bar graph shown in figure (c), Staircase
plot and scatter plot shown in figure (a) & (b) respectively.
Table 1 shows the numerical results of quality measure for the
simulated multicamera image.
If we observe the graph as
well as table for comparative analysis of quality measure
methods for multicamera images, it is observed that the
MIQM shows the better result and sensitivity compare to
other quality parameters. At the start of analysis when the
overlap is less, the PSNR showing the better result compare to
the MIQM but as we increase the amount of overlap the
sensitivity of PSNR goes on decreasing at that time the
MIQM shows the better result. We also tested our approach to
the Barbara images available in [5] which are shown in figure
5, here also we obtained the better result in contrast to others.
PSNR
VIF
MSSIM
MIQM
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Original
Image
Medium
Distorted
Image
High
Distorted
Image
1
1
1
1
0.3043
0.7820
0.4599
0.1890
0.3019
0.8100
0.4653
0.1754
Table.2. Comparative of Image Quality Measure Methods for geometrically
distorted standard Barbara Images.
Table 2 shows the comparative analysis for the standard
Barbara images with respect to different quality measure
techniques. Here we measure the quality of medium distorted
image & high distorted image with respect to original image.
For original image all the values are 1 but as we increase the
distortion the values gets decreases. Figure 6 shows the bar
graph for the image quality measure of standard Barbara
images.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 24 Number 3- June 2015
[10]
[11]
[12]
[13]
[14]
Fig.6. Image Quality Measure results in the form of Bar graph for
III. CONCLUSION AND FUTURE WORK
In this paper, we proposed the full reference objective
quality measure for assessment of the perceptual quality
of multicamera image for geometric distortion. In the past
only few works has been found in indicating the problem
of geometric distortion for multicamera image. The
proposed measure is based on the Luminance and contrast
index, spatial motion index and edge based structural
index. The Experimental results show that how it is
giving the better results in contrast to PSNR, VIF and
MSSIM. In the initial analysis the PSNR is giving the
better result but as we are increasing the distortion the
sensitivity of the PSNR decreases sharply but in that case
our approach is indicating the better result as shown in
the various graphs. In future we can use the databases like
the images which are the combination of both
photometric and geometric distortion and the images
having the geometric distortion interms of pixel shifting.
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