Int. J. Sensing, Computing & Control
Vol. 2, No. 2, pp. 79-86, 2012
IJSCC
ISSN 2185-0763
www.ijsc2.org
Short Paper
An adaptive enhancement algorithm of low illumine color video
image
Yong Chen 1,⋆ , Peng Feng 1 , Jiayi Yang 1 and Zhengxiang Xie 2
1
Key Laboratory of Industrial Internet of things & Network control, MOE, Chongqing University of
Posts and Telecommunications, Chongqing, PR. China, 400065;
2
Chongqing Medical University, Chongqing, PR. China,400016
⋆
Corresponding author: [email protected].
Abstract: Aiming at limits of enhancement algorithm of low illumination color image,
and according to the quality characteristic parameters of the original video image to set up
the mathematical model of human visual contrast resolution compensation parameters, we
adopt the contrast resolution compensation method to adaptively compensate color video
image from three-channel respectively, therefore, enhancing the low illumination true color
video images. The experimental results show that this method can improve image contrast
and average gray, so it can dig out the the information of original image which is difficult to
recognize as well, and improve the image visual effect.
Keywords: low illumine; image enhancement; Contrast Resolution Compensation
1. Introduction
In the video surveillance system often have night vision or glimmer or special environment of low
light situations. As a result of the impact of imaging system and environmental illumination, low-light
level image has the characteristics of concentrated gray value, low contrast and SNR, with the result that
the effect of the image vision is dark and fuzzy [1]. To capture the low contrast video image, which is not
easily observed we need the image enhancement process to improve the image quality. After enhanced
image can be recognized by human eye or machine [2].
This paper, through the image of the average gray to establish mathematical model of the image contrast resolution adaptive compensation. Using contrast resolution compensate each RGB color channel
in order to enhance true color image.
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2. Image enhancement algorithm
2.1. Contrast resolution limit of the human visual
An image without gray difference is no contrast. Human visual contrast resolution can distinguish
a minimum gray value images known as JND(just noticeable difference). In this paper we follow the
algorithm described in [3] for the contrast resolution with background gray level and change rules as
follow (1):


22.98e−0.057x
0 ≤ x ≤ 47
Y (x) =
(1)
 1.683−0.0083x+3.376×10−5x2 64 ≤ x ≤ 255
Type Y (x) expresses the human visual contrast resolution, x expresses the background gray. For the 0
to 255 gray level image. We define gray values in the range of 0 to 47 as scotopic vision and gray values
in the range of 64 to 255 is photopic vision. Equation (1) shows the JND performance is nonlinear,
contrast resolution function based human vision are different in the photopic and scotopic vision. The
results accord with human visual resolution of physical and mental physics basic principle.
In the dark visual conditions, contrast resolution limit is larger, when the surrounding environment of
average gray level is 0, the human eye can distinguish target image under surrounding environment of
minimum gray difference for 23. In low light situations, because of the human eye contrast resolution
image information is masked by low contrast background. It is difficult for the human eye to distinguish
image information. It needs to compensate for scotopic vision image contrast resolution in order to dig
out hidden information. In order to achieve the purpose of identification by the human eye.
2.2. Contrast resolution compensation principle
In the global range minimum discernible gray level difference of 1.17. Therefore, we at least want
to compensate for more than a gray-scale differences in order to dig out the dark vision masking information. Defining the contrast resolution compensation degree is CDegree(Compensate Degree). Set the
level of compensation 1.5 ≤ CDegree .
Use automatic closed loop control system of proportional integral operation control thought. To the
human eye can distinguish the shades of difference and deviation of the shades of difference in the
original image we use compensation of the proportional operation on the dark vision image. When the
CDegree value of 1.5 is the contrast resolution compensation threshold Th. Less than the threshold, we
use the gray level difference proportion integral calculation. Greater than the threshold value, we use
the gray-level differences in the base value of the threshold value compensation proportional operation.
Proportional coefficient k value is 1.5. Define scotopic vision contrast resolution compensation algorithm
is as follows:






T G(x, y) =
OG(x, y)
k
OG−1
∑
JN D(i)


i=0


 G(T h) + 1.5 ∗ [OG(x, y) − G(T h)]
OG(x, y) = 0
OG(x, y) ≤ T h
(2)
OG(x, y) > T h
T G(x, y) (Target Gray) and OG(x, y) (Original Gray)respectively said pixel point coordinates (x, y)
place before the compensation of the original gray value and compensation target gray value. Value range
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is [0 255]. JND(i) is the gray level for the target gray value i that can exactly distinguished differences.
It is (1) type of Y (x). The k value is compensating proportion coefficient. Take a positive real number,
as a regulator of compensation depth variable parameters. Threshold value after compensation at the
following target gray :
G(T h) = k
T∑
h−1
JN D(i)
(3)
i=0
For 8-bit digital system. When after contrast resolution compensation,the target gray value are more
than 255, make its equal to 255. Less than 1, make its equal to 0. Otherwise, there will be complementary
inverted (originally brighter place but will darken). Therefore, the need to give the formula (2) to the
following constraint conditions:

 255 T G(x, y) > 255
T G(x, y) =
 0
T G(x, y) < 1
(4)
For color image acquisition in a low light environment, according to the contrast resolution compensation principle, RGB trichromatic respectively do join threshold compensation. After the compensation
of the image not only can dig out the covered information, but also has color information. With different
values of k the compensation effects are different, which is shown in Figure 1.
Original image
AG=15.82
k=0.5
k=0.3
k=0.7
Figure 1. Different compensation factor k compensate results
Under the same gray background, as the k value increased compensation effect gradually getting
better, then gradually worse. It has the convex function characteristic. In the same background gray,
having an optimal compensation depth k value makes the image effect best. The value of k automatic
optimization modeling method as shown in Figure 2.
Take twenty images which are collected under low-light environments. This images average gray
level between 0 to 47. Take different scale factor k value to compensate. Compensated image average
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Int. J. Sensing, Computing & Control, Vol. 2, No. 2, 2012
Original image
Take different proportion
coefficient k value
compensation
Compensated
image
Extract character
Subjective evaluation
Best compensated
image
Average gray AG
Extract compensation
coefficient k
Mathematical model
Figure 2. Compensation ratio coefficient k automatic optimization model
gradation is between 20-140. Organization of 20 observers evaluate image. Select the best quality images
in each set of images. Using the method of mathematical statistics, discard deviation too large in each
group of 20 samples. Using mean value method to extract the final evaluation result. According to the
twenty groups of experimental data and mathematical statistics regression analysis method. We can draw
the curve as shown in Figure 3. Original abscissa is the average gray AG. Ordinate is the proportional
coefficient k. Dot in the figure is the experimental statistics point. Curve is fitting curve. Goodness of
fit R is 0.9329675. Fitting standard deviation is 0.32443. Characteristics of the function was the inverse
function.
Figure 3. Coefficient k and the original average gray relationship diagram
Building with optimized compensating scale factor k and average gradation of the original image AG
functional relationship is as follow :
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k = 2.9/AG + 0.28, 0 ≤ AG ≤ 47
(5)
Where (5) type is the optimum compensation function. We get the best quality compensation through
the best compensation function. Determine the compensation coefficient to obtain best image. Therefore, we get rid of the tedious manual optimization method. Achieved based on human visual contrast
resolution of image mining.
Using compensation coefficient k make automatic optimization. Get adaptive image.
Original
image
Extraction of
characteristic
parameters
AG
Automatic
optimization
compensation
ratio coefficient
k
Setting
threshold
Contrast
resolution
compensation
Compensated
image
Figure 4. Automatic optimization of the contrast resolution compensation
3. Image enhancement effect evaluation
In evaluating objective image quality, an image should have some basic physical characteristics that
can be measured. We use blind reference image quality evaluation to evaluate image. The mathematical
model is calculated as follows:
3.1. Average gray
If gray values concentrate in the vicinity of 0 or 255, the perception result is not satisfied. And good
image quality is linked to a certain appropriate global brightness[4]. AG denotes average gray level:
AG =
−1
N
−1 M
∑
∑
n=0 m=0
Gray(m, n)
M ×N
(6)
Where AG means the average gray-scale of an image, and Gray(m, n)is the gray value in pixel piont
(m, n). AG measures the basic average brightness or gray level in an image. The average gradation
of the image is too large or too small means image of light and dark. This images are not easy to be
observed.
3.2. Information entropy
The gray information contained in an image is richer,the quality is better. Here, define IE to describe
the amount of information in the objective image:
IE = −
255
∑
p(i) log2 p(i)
i=0
Where p(i) is the probability distribution of pixels in the gray-scale level of i .
(7)
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3.3. Average contrast
Contrast reflects the important information of an image for vision cognitive system.We describe average contrast of an image as follow:
ACc =
−2
M
−2 N
∑
∑
−
→
Cc (x, y)
x=0 y=0
(M − 1) × (N − 1)
(8)
−
→
Where Cc (x, y) is pixel(x, y) color gradient. The contrast is too high or too low the image are not
good quality image.
The above objective physical quantities constitute three parameters of image quality assessment. With
comprehensive evaluation of IE, AG and AC, a quantification model for NR-IQA has been established.
4. Experiment result and conclusion
In the Directshow platform [5], we write a Filter with the function of contrast resolution compensation. Let the filter chain into playback linked list and video captured linked list in the local video
file to deal with the local video files and real-time acquisition of the video. Using the USB interface
camera as input, acquisition frame rate is 30 frames per-second, and the display frame is more than 29
frames per-second after testing and disposing. After putting in the compensation algorithm, the frame
shift time is in 10 milli-seconds, and the offset time emerged when the program started. After normal
operation, it reached synchronization. Meanwhile, visual effects without delay phenomenon and can
meet the requirement of real-time.
Through the picture captured and saved, store the current playback frame of the original video frame
(Book-OI) and the video frame after compensated (Book-CRC). The Figure 5(a) and 5(b) show the original and compensated video frame, respectively. In order to further explain the compensation technology
in combination with rationality and superiority of characteristics by human visual system to realize the
image enhancement. We choose video frame Book-OI grabed as the original image. Using the common
color histogram equalization [6-7] and multi-scale Retinex to enhance static image. Figure 5(c) and 5(d)
Book-GHE and Book-MSR respectively show the enhanced images.
(a) Book-OI
(c) Book-GHE
(b) Book-CRC
(d) Book-MSR
Figure 5. The treatment effect of different enhancement algorithms
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As the chart shows, adopting the information entropy IE, the average gray AG and the average contrast
AC three parameters of the blind reference image quality evaluation to analyze the processed image
feature. From table 1, we can see that comprehensive analysis visual effect and characteristic parameters.
At the same time, Book-CRC adopts contrast resolution compensation and after compensated, AG,AC
increased while IE reduced. The essence of this method of contrast resolution compensation is that it
lost the information of original video image in exchange for contrast to dig out the information which
has not be seen by human visual perception.
Image information entropy achieve maximum after Book-GHE uses histogram equalization. But the
histogram equalization algorithm exists the phenomenon of too bright, and local details are not as effective as compensation method. It improve local contrast effect after Book-MSR uses multiscale-retinex
transformation [8-10]. But the information entropy of transformed image is larger than the original,
existing the fake information, and image noise is visible. Therefore, although compensation algorithm
lost part of the high gray level information, scotopic vision image passive target usually hides in strong
background environment. Exchanging information entropy for the gray-scale and contrast is reasonable
and make the compensated image visual effect is better than the other two methods.
Table 1. Algorithm performance evaluation parameters analysis
Book − OI
Book − CRC
Book − GHE
Book − M SR
IE
4.42
4.40
7.26
4.44
AG
AC
8.51
1.59
77.95 12.31
135.58 8.73
87.52 21.31
In this paper,we adopt the contrast resolution compensation method to adapt compensate color video
image from three-channel respectively. On the subjective, this method can effectively improve the contrast of low illumination image. Rich image detail. The color effect is more natural. On the objective, it
improve image brightness and contrast. Dig out the information which human eye can not discern. Compensation ratio coefficient optimization technology applied to adapt low-light color video monitoring. It
improve the utilization of information. And improve the visual effect of the monitoring system.
Acknowledgements
The work is supported by National Natural Science Foundation of China under Grant No.60975008.
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