Method for improving image quality
A method for improving image quality for display device has steps of inputting an image; computing a probability density function based on the image; generating a first luminance histogram; computing a weighing distribution function based on the first luminance histogram; generating a second luminance histogram through pixel redistribution of the first luminance histogram; computing a cumulative density function based on the second luminance histogram; computing a gamma conversion function based on the cumulative density function based on the second luminance histogram; and adjusting luminance distribution of the input image based on the gamma conversion function.
1. A method for improving image quality, comprising steps of:
inputting an image to any generic computer;
computing a probability density function based on the image to generate a first luminance histogram;
computing a weighing distribution function based on the first luminance histogram to generate a second luminance histogram through pixel redistribution of the first luminance histogram, wherein the weighing distribution function is defined as follows:
w
(
l
)
=
max
(
)
(
(
l
)
-
min
(
)
max
(
)
-
min
(
)
)
α
where l represents pixel of the image, l=lmin, lmin+1, lmin+2, . . . , lmax, PDFw(l) represents weighing probability density, max(PDF) represents maximum probability density, min(PDF) represents minimum probability density, and α represents an adaptive parameter that can be experimentally set to 0.5, the PDF(l) represents probability density function of l;
computing a cumulative density function based on the second luminance histogram, wherein the cumulative density function is defined as follows:
CDF
s
(
l
)
=
∑
h
=
0
l
w
(
h
)
∑
w
,
where l=lmin, lmin+1, lmin+2, . . . , lmax, ΣPDFw represents a sum of weighing probabilities, and CDFs(l) represents cumulative density function; and
computing a gamma conversion function based on the cumulative density function to adjust luminance distribution of the input image, wherein the gamma conversion function is defined as follows:
T
(
l
)
=
255
(
l
255
)
1
-
CDF
S
(
l
)
.
2. The method as claimed in claim 1 , wherein the step of inputting the image further comprises a step of:
obtaining a luminance histogram based on the image.
3. The method as claimed in claim 2 , wherein the step of computing the probability density function based on the image further comprises a step of:
computing the probability density function based on the luminance histogram.
4. A method for improving image quality, the method comprising steps of:
inputting predefined pattern images to any generic computer;
selecting a first incoming image from the images; and
performing a mapping curve generation modelling based on the first incoming image comprising:
computing a probability density function based on the first incoming image to generating a first luminance histogram;
computing a weighing distribution function based on the first luminance histogram to generate a second luminance histogram through pixel redistribution of the first luminance histogram, wherein the weighing distribution function is defined as follows:
w
(
l
)
=
max
(
)
(
(
l
)
-
min
(
)
max
(
)
-
min
(
)
)
α
where l represents pixel of the image, l=lmin, lmin+1, lmin+2, . . . , lmax, PDFw(l) represents weighing probability density, max(PDF) represents maximum probability density, min(PDF) represents minimum probability density, and α represents an adaptive parameter that can be experimentally set to 0.5, the PDF(l) represents probability density function of l;
computing a cumulative density function based on the second luminance histogram, wherein the cumulative density function is defined as follows:
CDF
s
(
l
)
=
∑
h
=
0
l
w
(
h
)
∑
w
,
where l=lmin, lmin+1, lmin+2, . . . , lmax, ΣPDFw represents a sum of weighing probabilities, and CDFs(l) represents cumulative density function; and
computing a gamma conversion function based on the cumulative density function to adjust luminance distribution of the input image, wherein the gamma conversion function is defined as follows:
T
(
l
)
=
255
(
l
255
)
1
-
CDF
S
(
l
)
;
and
performing a first entropy function based on the first incoming image, wherein the first entropy function is defined as follows:
H
(
l
)
=
-
∑
l
=
0
255
(
l
)
log
2
(
l
)
.
5. The method as claimed in claim 4 , wherein the step of selecting the first incoming image from the images further comprises step of:
obtaining a luminance histogram based on the first incoming image.
6. The method as claimed in claim 5 , wherein the step of computing the probability density function based on the first incoming image further comprises step of:
computing the probability density function based on the luminance histogram.
7. The method as claimed in claim 4 , wherein the step of selecting the first incoming image from the images further comprises:
storing the first incoming image.
8. The method as claimed in claim 4 , wherein after the step of performing a first entropy function based on the first incoming image, the method further comprises:
selecting an incoming image from the images if the images are not the first incoming image;
performing a second entropy function based on the incoming image, wherein the second entropy function is defined as follows:
H
(
l
)
=
-
∑
l
=
0
255
(
l
)
log
2
(
l
)
computing an absolute difference value based on the first entropy function and the second entropy function;
providing a threshold value; and
updating the first incoming image based on the incoming image if the absolute difference value is greater than the threshold value.
9. The method as claimed in claim 8 , wherein the step of performing the first entropy function based on the first incoming image comprises steps of:
computing a first probability density function based on the first incoming image; and
computing a first entropy function based on the first probability density function; and
wherein the step of performing the second entropy function based on the incoming image comprises steps of:
computing a second probability density function based on the incoming image; and
computing a second entropy function based on the second probability density function.