Television – Image falsification to improve viewer perception of... – Selective contrast expander
Reexamination Certificate
1998-04-22
2001-07-10
Kelley, Chris (Department: 2613)
Television
Image falsification to improve viewer perception of...
Selective contrast expander
C382S170000, C382S172000, C382S173000, C382S181000, C382S190000
Reexamination Certificate
active
06259472
ABSTRACT:
TECHNICAL FIELD
The present invention relates to a histogram equalization apparatus for contrast enhancement of a moving image and a method therefor, and more particularly, to a histogram equalization apparatus having a simple hardware and a method therefor.
BACKGROUND ART
The histogram of gray levels provides an entire description of the appearance of an image. Gray levels properly controlled with respect to a given image enhances the appearance or contrast of the image.
Histogram equalization is the most widely used and well-known among the methods for contrast enhancement. A method for enhancing the contrast of a given image according to the sample distribution of the image is disclosed in the following references: [1] J. S. Lim, “
Two
-
Dimensional Signal and Image processing
”, Prentice Hall, Englewood Cliffs, N.J., 1990, [2] R. C. Gonzalez and P. Wints, “
Digital Image Processing
,” Addison-Wesley, Reading, Mass. 1977.
Generally, since histogram equalization (so-called “distribution equalization”) has an effect for expanding the dynamic range, histogram equalization flattens the gray level distribution of the resultant image, so that the contrast of the image is enhanced.
Particularly, the histogram equalization in a medical engineering field as a method for distinct contrast between pixels of an image which is picked up indistinctly is very important in the recognition of the image.
Here, a typical histogram equalization method will be described briefly.
A given image {X} is composed of L discrete gray levels {X
0
, X
1
, . . . , X
L−1
}. Here, X
0
=0 represents a black level, and X
L−1
=1 represents a white level.
A probability density function (PDF) is defined by the following formula (1).
p
(
X
k
)
=
n
k
n
,
for
k
=
0
,
1
,
…
,
L
-
1
(
1
)
Here, n
k
represents the frequency of the grays level (X
k
) in the image {x}, and n represents the number of total samples (pixels) in the image {X}. Also, a cumulative distribution function (CDF) is defined by the following formula (2).
c
(
X
k
)
=
∑
j
=
0
k
p
(
X
j
)
(
2
)
An output (Y) of the typical histogram equalization with respect to an input sample (X
k
) of the given image is obtained by the following formula (3) based on the CDF.
Y=c
(
X
k
)
X
L−1
(3)
The histogram equalization method will be described in detail with reference to
FIGS. 1 through 3
.
FIG. 1
shows an example of a PDF of a specific image. That is, a luminance signal with a brightness of 0~255 gray levels is input, the number of pixels at each gray level is then counted, and the result is divided by the total number of pixels to obtain the result shown in FIG.
1
.
FIG. 2
shows a curve of the CDF obtained, based on the PDF of FIG.
1
. For example, when the value of the CDF corresponding to the gray level “100” at the point P is 0.875, which indicates that the number of pixels corresponding to 100 or less gray levels is 87.5% with respect to the input image.
FIG. 3
shows the PDF of the image passed through histogram equalization based on the CDF shown in FIG.
2
. That is, the level of the output signal (Y) after histogram-equalizing an input pixel Y
IN
is mapped into a level by the following formula (4).
Y=CDF value corresponding to Y
IN
×the maximum gray level (X
L−1
) (4)
For example, the output gray level after the input pixel having the gray level “100” has been histogram-equalized is mapped into 224 (=0.875×255) levels.
If the input image signal is an analog signal, the new PDF has a straight line (uniform distribution curve) having about 0.004 (=1/256) levels over the whole interval, like Q of FIG.
3
. However, if the input image signal is a digital signal, the histogram equalization is performed to a quantized level, and the result of
FIG. 3
is obtained. That is, assuming that the output gray level based on the CDF value (0.37) of
FIG. 2
is mapped into about “95” when the input gray level is “51”, and the output gray level based on the CDF value (0.47) of
FIG. 2
is mapped into about “120” when the input gray level is “52”, the output gray level of the input image signal having a gray level between “51” and “52” should be mapped into between about 95 and 120. However, a gray level between “51” and “52” does not exist when quantization is performed, so that a uniform distribution curve cannot be obtained.
Thus, as can be seen from
FIGS. 1 and 3
, the luminance level of the input image concentrated between the gray levels “50” and “100” is mapped into an extended luminance level having the gray levels between 10 and 200, so that the contrast is enhanced.
However, the above-described histogram equalization method has been applied to a still image to improve the image recognition capability and the contrast of the still image due to its problems related to the processing time required for obtaining the values of PDF and CDF and the hardware thereof, which causes a problem in that a frame memory for storing a frame image and a hardware-rich dividing circuit for real-time processing are required when being applied to a moving image.
DISCLOSURE OF THE INVENTION
To solve the above problems, it is an object of the present invention to provide a histogram equalization apparatus for contrast enhancement of a moving image using a simple hardware.
It is another object of the present invention to provide a method for enhancing contrast by performing a real-time histogram equalization on a moving image such as a TV or VCR video signal.
To achieve the first object, there is provided a histogram equalization apparatus for contrast enhancement of a moving image expressed by a predetermined number of gray levels. In the histogram equalization apparatus, a calculator counts the number of pixels having a gray level from the minimum to the maximum or less with respect to an input image in units of a frame to calculate a cumulative distribution function (CDF) value of each gray level, and a memory updates histogram-equalized levels corresponding to each gray level of the input moving image in units of a frame based on the CDF value of each gray level, and outputs a corresponding histogram-equalized level according to a level of the input moving image.
To achieve the second object, there is provided a histogram equalization method for contrast enhancement of a moving image expressed by a predetermined number of gray levels, comprising the steps of: (a) counting the number of pixels having a gray level from the minimum to the maximum or less with respect to an input moving image in units of a frame to output a CDF value of each gray level; and (b) multiplying the CDF value corresponding to the gray level of the input moving image by the maximum gray level to output a histogram-equalized level.
REFERENCES:
patent: 4680628 (1987-07-01), Wojcik et al.
patent: 5164993 (1992-11-01), Capozzi et al.
patent: 5862254 (1999-01-01), Kim et al.
patent: 5937090 (1999-08-01), Kim
patent: 5963665 (1999-10-01), Kim et al.
patent: 5995656 (1999-11-01), Kim
An Shawn S.
Kelley Chris
Samsung Electronics Co,. Ltd.
Sughrue Mion Zinn Macpeak & Seas, PLLC
LandOfFree
Histogram equalization apparatus for contrast enhancement of... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Histogram equalization apparatus for contrast enhancement of..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Histogram equalization apparatus for contrast enhancement of... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2523079