Quality Analyses of Fine Structures Transfer in the Photorealistic Images

doi:10.4236/ojapps.2013.31B002

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Open Journal of Applied Sciences, 2013, 3, 6-9

Published Online March 2013 (http://www.scirp.org/journal/ojapps)

Quality Analyses of Fine Structures Transfer

in the Photorealistic Images

S. V. Sai, I. S. Sai, N. Yu. Sorokin

Department of automation and information technologies, Pacific National University, Khabarovsk, Russia

Email: [email protected], [email protected]u

Received 2012

ABSTRACT

This work presents method of the quality analyses of the photorealistic images. Developed method utilizes proposed

criteria of image definition quality for fine details. Presented method has the following significant property: the quality

estimation is provided without test images or patterns. Algorithm for search and recognition of fine structures in the

photorealistic images using the predefined criterion is considered.

Keywords: Image Quality Analyses; Search Algorithm; Fine Structures Recognition

1. Introduction

Quality control of the compressed digital image is com-

plex and a mbiguous. Usually the distortion definitions of

the static and dynamic test signals are the indirect values

of the quality estimation of the broadband digital system.

However, systems of the compressed television signals

have to be analyzed using more complex parameter

measurements. Image quality in such systems changes

dynamically depending on the data rate, complexity of the

transferred image and applied coding algorithm. Static test

signals cannot present real quality characteristics of the

image. More complex analyses instead of simple test

signals must be carried out, i.e. natural test images in

compressed mode must be used.

Objective analyses of the digital video signals can be

produced using the current measurement systems (like

PQA200, PQA300, PQA500, etc.). In general, these types

of equipment are used by the vendors of the video com-

pression systems for the quality analyses of codecs. But

the application of such equipment is not suitable when the

multimedia system has been already selected by the

broadcasting company using some criteria.

Suppliers of the multimedia services provide highly

compressed images in a low quality pack because of ab-

sence of the standard methods of quality analyses for

digital images and video. In most cases user has no orig-

inal image to compare with, thus he estimates the “good”

or “bad” quality of the received image using his expe-

rience of visual perception of such properties as image

definition, s ha r p ne s s, contrast, saturation, presence of the

artifacts. Modern analyzers use the quality analyses me-

thods that are based on comparison of the differences

between test and distorted (corrupted) images [1-6]. This

comparison is carried out for some selected visual model.

Thus, the absence of test images does not allow the ap-

plication of such analyzers.

Therefore, finding new methods of the quality analys-

es is an actual problem. Such methods must provide an

objective estimation of image definition quality and

sharpness degradation without the test patterns.

This article proposes the method of the visual image

definition quality estimation of the photorealistic image

using the objective criterion without the test image. Spe-

cial features of the proposed algorithm for search and

recognition of fine structures in the real ima ge s are con-

sidered.

2. Algorithm for search and recogn it ion

In order to estimate the image definition quality search

and recognition of the fine structures must be carried out.

Such structures include fine details like dot objects or

thin line fragments with the size from one to several pix-

els.

Search algorithm consists of the following stages. In

the first stage the transformation of the primary color

signals (RGB) to the equal color space

***

VUW

produced for each pixel [7] using the following equation:

1725 31 −= /* YW

)uu(WU

−= 13

)vv(WV

−=13

whe re

is the luminance, changed from 1 to 100,

– is the br i ght ness i nd e x;

and

– are the

chromaticity indices; u and v – are the chromaticity

S. V. SAI ET AL.

coordinates in Mac-Adam diagram [8]; o

and o

–

are the chromaticity coordinates of basic white color with

u= 0,201, o

v = 0,307.

Equal color spaces (

***

VUW

, *** vaL , etc.) are used

traditionally for the estimation of color transfer quality

for the big details. Here estimation of the color differ-

ences Δ is computed as

() () ()

222

3***VUW

∆∆∆∆

++=

, (1)

whe re

WW −=

∆

;

UU −=

∆

;

VV −=

∆

;

VUW

− are the color coordinates

of the big detail from the test image;

− are the

color coordinates of the distorted image. The color trans-

fer quality is determined by the number of the minimum

perceptible color difference (MPCD). If Δ < 1 then the

color differences are invisible for the human eye.

In our works [9-10] we proposed method of the color

differences estimation between the fine detail (

VUW

)

and the color coordinates of the background pixels

(

VUW

) using the normalized equal color space. The

following criterion was used:

K











−











−











−

∆∆∆

∆

, (2)

whe re ΔK − color contrast of the fine detai l relative ly to

bac kgr o u nd ; *

∆

, *

∆

, *

∆

− are the thresholds

according to brightness and chromaticity indices for fine

detail. Threshold values on brightness and chromaticity

indices depend on the size of fine detail, background

color coordinates, time period of object presentation and

noise level. For fine details with sizes not exceeding one

pixel the threshold values are obtained experimentally. In

particular [9], for fine details of the test pattern located

on a grey background (9070 << *

W) threshold values

are approximately 6≈

∆

MPCD and

72≈≈ *

th UU

∆∆

MPCD.

In the second stage the criterion (2) is used for the es-

timation of quality and recognition of fine details. For

this, the processed image is scanned with the 3×3 pixels

window. Recognition algorithm of the fine structure is

executed for each iteration step.

During the analyses we should recognize an object: is

it a dot of a thin line. For this we use the binary images

of the fine structures presented on Figure 1. Using im-

ages we can obtain the spatial coordinates of the object

and background.

Figure 1. Binary images of the fine structures.

Analyses are started with the first structure – a dot (left

image on Figure 1). On the first step the mean values of

the color coordinates of the background (

VUW

) and

object (

VUW

) are computed.

Next, the following condition is checked:

nb <= ∑

∆∆

, (3)

whe re n − is a relative number of the background pixel in

the cur r e nt window (3×3); N − is a total number of back-

ground p ixel s in the win dow; n

∆

− is a color contrast

of the pixel relative to mean value of the background

color, computed as

K











−











−











−

∆∆∆

∆

. (4)

Condition (3) means that the color differences between

the background pixels are invisible for an eye. Similarly,

the color differences for the object pixels are computed:

mo <=

∑

∆∆

, (5)

whe re m − is a relative number of the object pixel; M − is

a total number of object pixels in a wind ow (3×3);

∆

− color contrast of the object pixel relative to mean

values (*

), that are produced similar to (4).

If the cond itions (3) a nd (5) are fulfilled, the contrast of

the object relatively to the background is computed

b/o

K











−











−











−

∆∆∆

∆

(6)

and afterwards the following condition is checked

2≥

b/o

∆

. (7)

If (7) is satisfied than the decision is made that the dot

object is recognized and we save the spatial coordinates

of the center of this object (i, j). Af t er this we move th e

win dow further by three pixels and ana lyze a no t her block

of the processed image.

If conditions (3), (5) and (7) are not fulfilled the next

structur e (a fragmen t of the hori zo nt a l thin line) is

processed. Also, if conditions (3), (5) and (7) are not ful-

filled for each structure we decide that the current win-

dow has no recognized objects and the window is shifted

further by one element.

Hereby, the proposed algorithm allows for recognition

and selection of the fine structures – dots and thin lines

fragments, noticeable for an eye. Note that compared to

the algorithm described in the work [10] we do not use

the comparison of the binary blocks to the binary masks.

This reduces the number of computation steps signifi-

cantly.

Figure 2 presents an example of the algorithm output,

S. V. SAI ET AL.

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,RR N

1, (8)

whe re M − is the number of processed images. This mean

value is compared to the threshold NTH

THRNN ≥

. (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

S. V. SAI ET AL.

to the series of video frames similarly to the photorealis-

tic images. In this case, the estimation due to criterion (9)

helps to decide if the image definition quality corres-

ponds to the utilized video format.

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