S. V. SAI ET AL.
Copyright © 2013 SciRes. OJAppS
where the fine structures are selected from the photorea-
listic image (244×225 pixels).
Figure 2. Example of the recognition of fine structures.
3. Criterion of image definition quality
Normally, the image de finitio n quality is estimated usin g
the resolution of the video system, i.e. by the number of
reproduced pixels or the format of the image. For exam-
ple, the format 1280×960 (1,23 Megapixels) means that
the photo or video system is able to reproduce the fine
details with sizes 1/1280 and 1/960 from width and
height of the image frame, accordingly. So, the image
will have a number of fine structures noticeable by an
eye in case the image is not distorted. Particularly, for the
image in Figure 2 number of recognized fine structures
is equal to NR = 0,18% from the total amount of pixels.
Obvio usl y, that NR depends on the real number of fine
details of the image and on the format of the image.
However, the photorealistic image will always have
some minimal value of NR. This assumption is used in the
developed method of the image definition quality estima-
tion.
The method of the estimation consists of several stages.
Assume that we have a number of photorealistic images
provided by the multimedia service through the Internet.
Usually, these images are transferred using the com-
pressed format, like JPEG or JPEG-2000 standards. Each
i mage (m) can be presented in arbitrary format (number
of pixels). For example, one of them can have 1280×960
pixels, another 800×600 pixels.
At the first stage, we process each image with the pre-
sented algorithm for search and recognition of the fine
structures. We compute the mean value:
∑
=
=
M
m
m,RR N
M
N
1
1, (8)
whe re M − is the number of processed images. This mean
value is compared to the threshold NTH
. (9)
If the criterion (9) is satisfied then the decision is
made that the image definition quality corresponds to the
presented format. This threshold value is selected expe-
rimentally after analyzing a big set of not distorted im-
ages in different formats. As a result of the experiment
we concluded that the image definition quality corres-
ponds to the presented format in case the number of rec-
ognized fine structures is greater than the threshold NTH =
0,05%.
If the criterion (9) is not satisfied, i.e. number of fine
structures is less than 0,05%, the decision is made that
the image definition quality does not correspond to the
presented format.
Nonfulfill ment of the criterion (9) means that the
processed images have lack of fine structures. This result
can be explained by the following reasons: images were
highly distorted due to the high level of compression in
codec, or were obtained from the digital camera with the
lower resolution than the image format.
As an example Figure 3 shows the result of image
(from Figure 2) analyses after 2D Gaussian filtering.
During the analyses the number of recognized fine struc-
tures is equal to NR = 0,02% and it does not satisfy crite-
rion (9).
Figure 3. Analyses of the distorted image.
4. Conclusion
Let ’s emphasize the ma in feat ur es of the developed me-
thod of the analyses of the image definition quality for
the photorealistic images compared to the known tech-
niques.
Analyses are carried out based on the developed algo-
rithm for search and recognition of the fine structures in
i mages. Main property of the algorithm is that the recog-
nition process is done using the MPCD of the fine details.
Also the author’s method of estimation of the color dif-
ferences in the normalized equal color space is applied.
The o utp ut fr om the algo ri thm contains the percent
number of the recognized fine structures (NR) that are
noticeable by an eye. The value NR is used in the pro-
posed criterio n (9) for the estimation of the real image
definition quality of the photorealistic image.
Thus, compared to the known techniques our method
does not require test images or patterns (containing fine
structures, e.g., like dash lines), which is the main dif-
ference.
Developed criterion can be used for the video quality
analyses. In his case, the same method should be applied