1 chapter 4 color in image and video 4.1 color science 4.2 color models in images 4.3 color models...
TRANSCRIPT
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Chapter 4Color in Image and Video
4.1 Color Science4.2 Color Models in Images4.3 Color Models in Video4.4 Further Exploration
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Chapter 4Color in Image and Video
4.1 Color Science4.2 Color Models in Images4.3 Color Models in Video4.4 Further Exploration
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Light and Spectra Light is an electromagnetic wave. Its color is
characterized by the wavelength content of the light. Laser light:
consists of a single wavelength: e.g., a ruby laser produces a bright, scarlet-red beam.
Most light sources: produce contributions over many wavelengths.
Visible wavelengths: humans cannot detect all light, just contributions that
fall in the “visible wavelengths". From Blue to Red:
Short wavelengths produce a blue sensation, long wavelengths produce a red one.
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E() White light contains all the colors of a rainbow.Visible light is an electromagnetic wave in the range 400 nm to 700 nm“nm” stands for nanometer (10−9 meters)
Daylight (white light)
E(): Spectral Power Distribution (SPD) or spectrum.
c.f. Human ear can hear 20 to 20K Hz.
https://www.youtube.com/watch?v=qNf9nzvnd1k
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Spectrophotometer
device used to measure visible light, by reflecting light from a diffraction grating (a ruled surface) that spreads out the different wavelengths.
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Human Vision Lens ( 水晶體 ) and Retina ( 視網膜 )
The eye works like a camera. Lens focus an image onto the retina
(upside-down and left-right reversed). Rods ( 柱狀體 ) and Cones ( 錐狀體 )
Rods: “all cats are gray at night!“ Three kinds of cones: most sensitive to
red (R), green (G), and blue (B) light. Eye-Brain System
The brain is good at algebra.
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Spectral Sensitivity Functions qR(), qG() and qB()
Spectral Sensitivity Curves(1)
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Fig 4.3 R, G, B cones, and Luminous Efficiency Curve V()
Spectral Sensitivity Curves (2)
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6 millions of cones proportion of R:G:B ~ 40:20:1 ( 已計入 q())
Luminous Efficiency Function V () sum of the response curves for R, G, and
B. The rod sensitivity curve:
looks like the luminous-efficiency function V () but is shifted to the red end of the spectrum.
Luminous Efficiency Function
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These spectral sensitivity functions q () = (qR(), qG(), qB())T (4.1)
The response in each color channel in the eye is proportional to the number of neurons firing.
R = E() qR() d G = E() qG() d B = E() qB() d (4.2)
Spectral Sensitivity of the Eye
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Surface Reflectance
S(): reflectance function
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The equations that take into account the image formation model are:
R = E() S() qR() d
G = E () S() qG() d
B = E() S() qB() d (4.3)
Image formation
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(break)
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Color-Matching Functions A technique evolved in psychology
Matching a combination of basic R, G, and B lights to a given shade
(even without knowing the eye-sensitivity curves of Fig.4.3)
Color Primaries 700, 546, 436 – nm (700, 546.1, 435.8) 580, 545, 440 – nm Error / mistake?
課本這段是錯的, Google “CIE 1931”
INTERNATIONAL COMMISSION ON ILLUMINATION
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Fig 4.8 Colorimeter experiments
700 nm 546 nm 436 nm
Colorimeter
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Fig 4.9 CIE RGB color matching functions
CIE 1931 2° Matching Func.
700 nm 546 nm 436 nm
Radiant power72 : 1.3791 : 1
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Spectral Sensitivity Functions qR(), qG() and qB()
Recall: Spectral Sensitivity
=560, for example
( )( )
( ) ?? ( )
( ) ( )
R
G
B
rq
q g
q b
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Fig 4.9 CIE RGB color matching functions
Recall: CIE 1931 2°
700 nm 546 nm 436 nm
=560, for example
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Relation Between 2 Exps.
( )( ) (700) (546) (436) 72 0 0
( ) (700) (546) (436) 0 1.38 0 ( )
( ) (700) (546) (436) 0 0 ( )
R R R R
G G G G
B B B B
rq q q q a
q q q q a g
q q q q a b
單光源刺激度
加乘轉換矩陣 (M)三色光轉換成各別刺激度再加總
指指指指指指指配色比
單位能量
M [ E() E() E() ]T
=436 r(436)=0, g(436)=0, b(436)>0=546 r(546)=0, g(546)>0, b(546)=0=700 r(700)>0, g(700)=0, b(700)=0=560 ...
=560
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[R G B] Stimulus
( )( ) (700) (546) (436) 72 0 0
( ) (700) (546) (436) 0 1.38 0 ( )
( ) (700) (546) (436) 0 0 ( )
R R R R
G G G G
B B B B
rq q q q a
q q q q a g
q q q q a b
( ) ( )
( ) ( )
( ) ( )
R
G
B
E q dR
G E q d
B E q d
72 (700) ( )
( ) ( ) ( ) 1.38 (546) ( ) ( ) ( ( ))
(436) ( )
R
R R
R
a q r
R E q d E a q g d E F r d
a q b
(1) RGB 單一色彩的刺激 也是暫定三原色的組合
( 紅色的刺激不能只靠暫定紅光模擬 )
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645 nm 526 nm 444 nm
Normalized byRadiant powerto 1 : 1 : 1
CIE 1964 10° Matching Func.
(2) 另取「暫定三原色」亦無法改變紅光的負值
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Grassman's Law Additive color
results from self-luminous sources lights projected on a white screen phosphors glowing on the monitor glass
Additive color matching is linear. color1 a linear combinations of lights color2 another set of weights, then combined color: color1+color2 the sum of the two sets of weights.
(3) 配色函數無法呈現色光的加法定理
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Grassman's Law (Example) Example
color1 0.2 X + 0.6 Y + 1.2 Z color2 1.8 X + 0.9 Y + 0.3 Z color1+color2 2 X + 1.5 Y + 1.5 Z
Example 2 ( 線性內插 : 假設色彩有坐標 ) color1 (0.2 X + 0.6 Y + 1.2 Z) /2 color2 (1.8 X + 0.9 Y + 0.3 Z) /3 (1/2) color1+ (1/2) color2 0.35 X + 0.3 Y + 0.35 Z
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Drawback of the RGB Color Matching Functions
RGB stimuli are dependent on linear combinations of r(), g() , b() , but not purely respective values of them.
r() color-matching curve has a negative lobe
Thus we cannot conveniently indicate a combined color following Grassman’s law
qR(575)!= qR(550)/2+qR600)/2
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Fig 4.10 CIE standard XYZ color matching Functions x(), y(), z().
CIE-RGB to CIT-XYZ
下一頁:改用「坐標軸」來看待「調色」
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Linear Transform (RGB-XYZ)
( ) ( )2.7689 1.7517 1.1302
( ) 1 4.5907 0.0601 ( )
0 0.0565 5.5943( ) ( )
x r
y g
z b
由「暫定三原色」經線性轉換,定義出「虛擬三原色」。
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Fictitious Primaries
A 3 x 3 matrix away from r, g, b curves, and are denoted x(), y(), z(), as shown in Fig. 4.10
XYZ color-matching functions with only positives values
The middle standard color-matching function y() exactly equals the luminous-efficiency curve V () shown in Fig. 4.3.
The area under Each curve is the same.
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Orthogonal XYZ Color Space
Original Coordinate before Normalization
Pure Colors without Combination
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CIE Chromaticity Diagram
z = 1 – x – y
( ) ( )
( ) ( )
( ) ( )
E x dX R
Y E y d T G
Z BE z d
(4.7)
(4.8)
(4.6)
(4.9)
(4.10)
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Fig 4.11
SpectrumLocushorseshoe
Line ofpurples
Recall:Grassman’s Law
A
B
C
(x,y) Chromaticities
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Chromaticities and White points of Monitor Specification
Table 4.1
4.1.9
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Out-of-Gamut Colors
Fig. 4.13: Approximating an out-of-gamut color
4.1.10
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(break)
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Out-of-Gamut Colors
Fig. 4.13: Approximating an out-of-gamut color
4.1.10
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More Color Models
YUV system (additive) YUV, YIQ, YCbCr color Models
Munsell system (additive) L*a*b* (CIELAB), Munsell color naming system
HSI -- Hue, Saturation and Intensity; HSI, HSL, HSV, HSD, HCI color models Lightness/Value/Darkness/Chroma
CMY system CMY, CMYK color models Cyan/Magenta/Yellow/blacK
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More Color Models
YUV system (additive) YUV, YIQ, YCbCr color Models
Munsell system (additive) L*a*b* (CIELAB), Munsell color naming system
HSI -- Hue, Saturation and Intensity; HSI, HSL, HSV, HSD, HCI color models Lightness/Value/Darkness/Chroma
CMY system CMY, CMYK color models Cyan/Magenta/Yellow/blacK
傳輸格式
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RGB YUV Y= 0.229 R + 0.587 G + 0.114 B
U=B-Y V=R-Y
Y 0.229 0.587 0.114 R
U = -0.147 -0.289 0.436 G
V 0.615 -0.515 -0.100 B
R 1 0 1.14 Y
G = 1 -0.39 -0.58 U
B 1 2.03 0 V
Y=1 0.229 0.587 0.114 R=1
U=0 = -0.147 -0.289 0.436 G=1
V=0 0.615 -0.515 -0.100 B=1
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YUV Decomposition
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Y channel (R+G+B)/3
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YIQ Color Model YIQ is used in NTSC color TV broadcasting
“Y “ in YIQ is the same as in YUV; I and Q are a rotated version (33° ) of U and V .
I
Q
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YIQ Transform Matrix
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YCbCr Color Model
ITU Standard (International Telecommunication Unit)
ITU-R BT. 601-4 (also known as “Rec. 601”) Closely related to the YUV transform
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YCbCr Transform Matrix
Theoretical:
Digital Processing: (16~235/ ± 112+128)
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More Color Models
YUV system (additive) YUV, YIQ, YCbCr color Models
Munsell system (additive) L*a*b* (CIELAB), Munsell color naming system
HSI -- Hue, Saturation and Intensity; HSI, HSL, HSV, HSD, HCI color models Lightness/Value/Darkness/Chroma
CMY system CMY, CMYK color models Cyan/Magenta/Yellow/blacK
美術命名
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Fig 4.14 CIELAB Model
Max a* RedMin a* Green
Max b* YellowMin b* Blue
Outside solidMore colorful(saturate)
L*a*b* (CIELAB) Color Model
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Munsell Color Naming System
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More Color Models
YUV system (additive) YUV, YIQ, YCbCr color Models
Munsell system (additive) L*a*b* (CIELAB), Munsell color naming system
HSI -- Hue, Saturation and Intensity; HSI, HSL, HSV, HSD, HCI color models Lightness/Value/Darkness/Chroma
CMY system CMY, CMYK color models Cyan/Magenta/Yellow/blacK
影像處理
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Gonzalez. Chapter 6Color Image Processing
Gonzalez. Chapter 6Color Image Processing
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Gonzalez. Chapter 6Color Image ProcessingGonzalez. Chapter 6Color Image Processing
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Gonzalez. Chapter 6Color Image ProcessingGonzalez. Chapter 6Color Image Processing
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Gonzalez. Chapter 6Color Image ProcessingGonzalez. Chapter 6Color Image Processing
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Gonzalez. Chapter 6Color Image ProcessingGonzalez. Chapter 6Color Image Processing
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Gonzalez. Chapter 6Color Image ProcessingGonzalez. Chapter 6Color Image Processing
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1
1/ 22
1( ) ( )
2cos
if
360 if
31 [min( , , )]
( )
1( )
3
( )( )( )
R G R B
B GH
B G
S R G BR G B
I R G B
R B G BR G
H.S.I 彩色轉換
If 0 H <120 ,
cos1
cos(60 - H)
3 ( )
(1 )
S HR I
G I R B
B I S
If 120 H <240 ,
(1 )
cos1
cos(60 - H
H = H-12
)
)
0
3 (
R I S
S HG I
B I R G
If 240 H 360 ,
3 ( )
(1 )
cos1
cos(60 -
H = H-24
0
H)
R I G B
G I S
S HB I
建議以上 RGB 值在實作時應取Value = min(255, floor(…))以避免 overflow 而敗給其他兩色
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Recall: YUV Decomposition
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HSI Decomposition (I, S, H)
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圖 10-13 圖 10-10 之飽和度強化 (Saturation enhancement)
圖 10-10 彩色影像
彩色影像強化—增加飽和度
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More Color Models
YUV system (additive) YUV, YIQ, YCbCr color Models
Munsell system (additive) L*a*b* (CIELAB), Munsell color naming system
HSI -- Hue, Saturation and Intensity; HSI, HSL, HSV, HSD, HCI color models Lightness/Value/Darkness/Chroma
CMY system CMY, CMYK color models Cyan/Magenta/Yellow/blacK
彩色印刷
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(1,0,0)
(0,1,0)
(1, 1, 0)
(0,1,1) (0,1,0)(0, 0, 1)
Additive v.s. Subtractive Models
(1,0,0)
(0, 0, 1)
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Complementary Color
I = 0.5 的平面上Hue 相差 180的補色示意環
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RGB Color MixingColor 1 : Color 2 = a : b
Result
( ) ( )a b
a b a b
1 2
1 2
1 2
R R
G G
B B
e.g. Color 1 = (0.8, 0.4, 0.2) Color 2 = (0.5, 0.6, 0.8) …
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CMY Color MixingDefinition:
Color 1 : Color 2 = a : b
1 ( ) ( ) ( ) ( )a b a b
a b a b a b a b
1 2 1 2
1 2 1 2
1 2 1 2
1-C 1-C C C
1-M 1-M M M
1-Y 1-Y Y Y
色料混合 = 兩色補色相加之後的補色
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Fig 4.15
RGB cube CMY cube
RGB and CMY color cubes
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Undercolor Removal: CMYK
Sharper and cheaper printer colors Calculate that part of the CMY mix that
would be black, Remove it from the color proportions,
and add it back as real black.CMYK Model
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Printer Gamuts “Crosstalk" between the color channels make it
difficult to predict colors achievable in printing.
Fig 4.17 (a) Block Dyes (b) Block Dyes Gamut and NTSC Gamut