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Understanding Natural Colors in Image and Video Processing

Explore the science of color, human eye perception, and various color models. Learn about light wavelengths, eye sensitivity, color matching functions, and more in image and video processing.

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Understanding Natural Colors in Image and Video Processing

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  1. Chapter 4Color in Image and Video 4.1 Color Science 4.2 Color Models in Images 4.3 Color Models in Video 4.4 Further Exploration

  2. 1. 認識自然界的色彩 2. 認識人眼辨色過程 3. 定義各種色彩模型 Chapter 4Color in Image and Video 4.1 Color Science 4.2 Color Models in Images 4.3 Color Models in Video 4.4 Further Exploration

  3. 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.

  4. Daylight (white light) 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) E(): Spectral Power Distribution (SPD) or spectrum. • c.f. Human ear can hear 20 to 20K Hz. • https://www.youtube.com/watch?v=qNf9nzvnd1k

  5. Electromagnetic Wave

  6. Spectrophotometer device used to measure visible light, by reflecting light from a diffraction grating (a ruled surface) that spreads out the different wavelengths.

  7. 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. • 6 millions of cones • proportion of R:G:B ~ 40:20:1

  8. Spectral Sensitivity Curves(1) Spectral Sensitivity Functions qR(), qG() and qB()

  9. Spectral Sensitivity Curves (2) Fig 4.3 R, G, B cones, and Luminous Efficiency Curve V()

  10. Luminous Efficiency Function • 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. • Eye-Brain System • The brain is good at algebra.

  11. Spectral Sensitivity of the Eye • 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) 自發光源 e.g.黃色燈泡

  12. Surface Reflectance 光的反射體 S(): reflectance function

  13. Image formation • 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)

  14. (break)

  15. Color-Matching Functions • A technique evolved in psychology • Matching a combination of basic R, G, and B lights to a given shade (Wright & Guild, 1931) • (汞蒸氣最強光譜, 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

  16. Colorimeter 700 nm 546 nm 436 nm Fig 4.8 Colorimeter experiments

  17. CIE 1931 2° Matching Func. 700 nm 546 nm 436 nm Radiant power 72 : 1.3791 : 1 Fig 4.9 CIE RGB color matching functions

  18. Recall:Spectral Sensitivity l=560, for example Spectral Sensitivity Functions qR(), qG() and qB()

  19. Recall: CIE 1931 2° 700 nm 546 nm 436 nm l=560, for example Fig 4.9 CIE RGB color matching functions

  20. ? results input l=560 [ E(700) E(546) E(436) ]T M 加乘轉換矩陣 (M) 三色光轉換成各別 刺激度再加總 單光源 刺激度 單位能量 指定 l波長 對應 700/546/436 之配色比 l=560 l=436  r(436)=0, g(436)=0, b(436)>0 l=546  r(546)=0, g(546)>0, b(546)=0 l=700 r(700)>0, g(700)=0, b(700)=0 l=560  ...

  21. [R G B] Stimulus (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)

  22. CIE 1964 10° Matching Func. (2) 另取「暫定三原色」亦無法改變紅光的負值 645 nm 526 nm 444 nm Normalized by Radiant power to 1 : 1 : 1

  23. Grassman's Law (3) 配色函數無法呈現色光的加法定理 • 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.

  24. 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X,Y,Z=某些色彩) • 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

  25. 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+qR(600)/2

  26. CIE-RGB to CIE-XYZ Fig 4.10 CIE standard XYZ color matching Functions x(), y(), z(). 下一頁：改用「坐標軸」來看待「調色」

  27. Linear Transform (RGB-XYZ) 由「暫定三原色」經線性轉換， 定義出「虛擬三原色」。

  28. Fictitious Primaries • A 3 x3 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.

  29. Orthogonal XYZ Color Space (0,0,0) (0,0,0) • Original Coordinate before Normalization • Pure Colors without Combination

  30. CIE Chromaticity Diagram (4.6) (4.10) (4.7) (4.8) z = 1 – x – y (4.9)

  31. (x,y) Chromaticities Fig 4.11 Recall: Grassman’s Law Spectrum Locus horseshoe B 色光皆有座標 混色符合加法 及內插公式 A C Line of purples

  32. Chromaticities and White points of Monitor Specification 4.1.9 Table 4.1

  33. Out-of-Gamut Colors 4.1.10 Fig. 4.13: Approximating an out-of-gamut color

  34. (break)

  35. Out-of-Gamut Colors 4.1.10 Fig. 4.13: Approximating an out-of-gamut color

  36. 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

  37. 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

  38. RGB  YUV • Y= 0.229 R + 0.587 G + 0.114 B • U=B-Y • V=R-Y

  39. YUV Decomposition

  40. (R+G+B)/3 Y channel Choose what ?

  41. 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

  42. YIQ Transform Matrix

  43. YCbCr Color Model • ITU Standard (International Telecommunication Unit) • ITU-R BT. 601-4 (also known as “Rec. 601”) • Closely related to the YUV transform

  44. YCbCr Transform Matrix Theoretical: Digital Processing: (16~235/ ± 112+128)

  45. 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

  46. L*a*b* (CIELAB) Color Model Outside solid More colorful (saturate) Max b* Yellow Min b* Blue Max a* Red Min a* Green Fig 4.14 CIELAB Model

  47. Munsell Color Naming System

  48. 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

  49. Gonzalez. Chapter 6 Color Image Processing (0,0,1) (0,1,0) (1,0,0)

  50. Hue

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