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Digital Color Images Lecture on the image part (#1) Image Processing (IP2)

Digital Color Images Lecture on the image part (#1) Image Processing (IP2). Volker Krüger Aalborg Media Lab Aalborg University Copenhagen vok@media.aau.dk. Agenda. What are digital colors ? What are color spaces ? Why do we have different color spaces?. What is Color?.

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Digital Color Images Lecture on the image part (#1) Image Processing (IP2)

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  1. Digital Color Images Lecture on the image part (#1)Image Processing (IP2) Volker Krüger Aalborg Media Lab Aalborg University Copenhagen vok@media.aau.dk

  2. Agenda • What are digital colors ? • What are color spaces ? • Why do we have different color spaces?

  3. What is Color? • The colors that humans perceive are determined by the nature of the light reflected from an object! Green objects reflect “green” light! • Achromatic: Only intensities (amount of light) • Gray levels as seen on black/white TV-monitor • Ranges from black to white • Chromatic: Light waves; Visual range: 400nm-700nm

  4. Red, Green, Blue • R,G,B are called Primary Colors • R,G,B where chosen due to the structure of the human eye • R,G,B are used in cameras

  5. Receptivity of the Eye Cells

  6. R+G+B=White? Really?!? • So why don’t we get white, when we use paint? Subtractive Color! • But why does it work for the TV? Additive Color!

  7. Additive/Subtractive Color • Additive Color: Sum of light of different wave lengths. That light reaches our eye directly. • Examples: TV, Multimedia Projector • Subtractive Color: White Color is emitted by the sun and is only partly reflected from an object! • Red paint filters all light, except red! • Yellow paint absorbs blue, but reflects red and green • Examples: Paint

  8. RGB Color Space • The “classical” Computer Color space • 3 different colors: Red, Green, Blue • Similar to the human visual system! • If R,G,B have the same energy, we perceive a shade of white (grey, black).

  9. RGB Color Space A single pixel consists of three components: [0,255]. Each pixel is a Vector. (0,0) = Pixel-Vector in the computer memory Final pixel in the image Caution! Sometimes pixels are not stored as vectors. Instead, first is stored the complete red component, then the complete green, then blue.

  10. Example RGB R-Component Original Image G-Component B-Component

  11. ( ) + + / 3 = Convert color to grayscale • I = (R+G+B) / 3 ?

  12. Color and Intensity are mixedRGB to Chromaticities Chromaticity Plane Colour Cube Same Colour, different intensities • Used in Computer Vision: normalised RGB

  13. Another way of separating color and intensity: HSI • H=Hue S=Saturation I=intensity • H and S may characterize a color: Chromaticities • Hue: associated with the dominant wavelength in the mixture of light waves, as perceived by an observer. • Hue is color attribute that describes a pure color • Saturation: relative purity; amount of white light mixed with hue • Example: Pure colors are fully saturated. Not saturated are for example pink (red+white)

  14. HSI color space • Perhaps the most intuitive color representation! • Used in Computer Graphics (and computer vision)

  15. HSI Color Space A single pixel consists of three components. Each pixel is a Vector (0,0) = Pixel-Vector in the computer memory Final pixel in the image Caution! Sometimes pixels are not stored as vectors. Instead, first is stored the complete hue component, then the complete sat., then the intensity.

  16. Example HSI Hue Original Image Saturation Intensity

  17. YUV Color Space • YUV: used in commercial color TV broadcasting and video signals • We need a format that decouples grayscale and color: HSI • “Poor-man’s” HSI • Much easier to compute from RGB, than HSI

  18. YUV Color Space A single pixel consists of three components. Each pixel is a Vector (0,0) = Pixel-Vector in the computer memory Final pixel in the image Same Caution as before applies here!

  19. Example YUV Intensity Original Image U-Component V-Component

  20. Full Color / Pseudo Color • Full Color: acquired by a TV camera/scanner • Pseudo Color: Assigning a shade of color to a monochrome intensity or range of intensities

  21. Mapping of Gray Values into PseudoColor Images

  22. PseudoColor

  23. What to remember • Achromatic versus Chromatic • How come that with RGB we can represent almost all colors? • Subtractive Color versus Additive Color • Color Spaces • RGB: Used in cameras and the HSV • Normalised RGB: Decouples intensity and color • Used in Computer Vision • HSI: Decouples intensity and color (used in CG and CV) • YUV: Used in commercial color TV • Pseudo color: represent grayscales as colors

  24. Exercises • Questions to the lecture? • What was good about the lecture and what could have been better? • How many different 512x512 grayscale (8bit) images exist? • How many different colors exist for a 24bit pixel? • How many different 512x512 color (24bit) images exist? • How is color represented in HTML? • Start EyesWeb! Load the example patches under Eyesweb3.3.0\patches\video PE-Questions: explain in your own words: • Why can we use RGB to generate almost all colors? • What is the difference between Achromatic and Chromatic? • What is the difference between Subtractive Color and Additive Color? • Describe the four different color spaces (RGB, rg, HSI, YUV) • What are their characteristics and where are they used? • What is a pseudo color image?

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