Colors and Visual System (09) RNDr. Martin Madaras, PhD. - - PowerPoint PPT Presentation

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Colors and Visual System (09) RNDr. Martin Madaras, PhD. - - PowerPoint PPT Presentation

Aug 14, 2022 1.05k likes •1.66k views

Principles of Computer Graphics and Image Processing Colors and Visual System (09) RNDr. Martin Madaras, PhD. martin.madaras@stuba.sk Computer Graphics - Image Processing - Modeling - Rendering - Animation - Advanced Techniques 2 Computer

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Principles of Computer Graphics and Image Processing

Colors and Visual System (09)

  • RNDr. Martin Madaras, PhD.

martin.madaras@stuba.sk

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  • Image Processing
  • Modeling
  • Rendering
  • Animation
  • Advanced Techniques

Computer Graphics

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  • Image Processing
  • Image Representation
  • Modeling
  • Rendering
  • Animation
  • Advanced Techniques

Computer Graphics

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  • Raster Graphics
  • Image devices
  • Image representation
  • Human vision system
  • Colors

Image Processing

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  • Images
  • What is an image?
  • How to capture images?
  • How to display images?
  • Color
  • What is a color?
  • How do we perceive the color?
  • How computers represent the color?

Raster Graphics

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  • Images
  • What is an image?
  • How to capture images?
  • How to display images?
  • Color
  • What is a color?
  • How do we perceive the color?
  • How computers represent the color?

Raster Graphics

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  • Ask questions, please!!!
  • Be communicative
  • www.slido.com #PPGSO09
  • More active you are, the better for you!

How the lectures should look like #1

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Frame Buffer

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Color Mixing

  • Mixture of Red, Green and Blue
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Color Display

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Modern Displays

LCD - liquid crystal display

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Pixel

PIxEL - Picture Element

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Color Frame Buffer

Each color intensity needs to be stored separately

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Color Depth

Bits per pixel determine image color depth

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8-bit Palette

Each pixel points to a color number in palette Palette is 24bit but contains only 256 colors

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Display Resolution and Memory

  • Frame-buffer memory size and speed as limiting factor
  • 1024x768 24bit - 2.25 MB - 0.79 megapixels
  • 1920x1080 24bit - 5.94 MB - 2 megapixels
  • 4096x2160 30bit - 31.64 MB - 8.84 megapixels
  • For animated displays we need to read the frame buffer at

least 24 times per second

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Double Buffering

  • Q: What happens if we write directly to the framebuffer ?
  • We need a second buffer to solve this problem
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Aspect Ratio

  • Display aspect ratio
  • TV 4:3
  • HDTV 16:9
  • 35mm film 3:2
  • Pixel aspect ratio
  • Nowadays, almost always 1:1
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Frame-buffer Manipulation

  • Direct memory access
  • Limited by OS security policies
  • Various graphical toolkits and libraries
  • Often slow for complex geometry and 3D graph
  • OpenGL and DirectX
  • Fast but requires hardware
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  • Rectilinear 2D array of pixels

Reality Digital Image

What is an image?

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  • Rectilinear 2D array of pixels

Reality Digital Image

What is an image?

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  • Pixels are NOT little squares! Pixels are samples!

Reality Digital Image

What is an image?

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  • For a programmer it is a memory structure
  • Usually represented as sequence of pixels
  • Typically line after line, left to right
  • Pixels have their own structure

What is an image?

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  • Pixels are samples of a continuous function
  • Photoreceptors in eye
  • CCD chips in digital cameras
  • Rays in virtual scene

How to capture images?

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  • Re-create continuous signal from samples
  • i.e. CRT monitor

How to display images?

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  • Spatial resolution
  • Image has “Width” x “Height” pixels
  • DPI (dots per inch) is more representative
  • Intensity resolution
  • Each pixel has limited “Depth” bits per color
  • Temporal resolution
  • Image is updated at “Rate” Hz in case of a video sequence

Image resolution

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  • Images
  • What is an image?
  • How to capture images?
  • How to display images?
  • Color
  • What is a color?
  • How do we perceive the color?
  • How computers represent the color?

Raster Graphics

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  • Distribution of energies amongst frequencies of visible light

range

What is color?

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  • The perceived color of light is characterized by
  • Hue = dominant frequency (peak)
  • Lightness = luminance (area under curve)
  • Saturation = excitation purity (ratio of highest to rest)

White light Orange light

Visible light

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  • Color as perceived reflectance of the light source

How do we perceive color?

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  • Color as perceived reflectance of the light source

How do we perceive color?

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  • The density of cones is not linear
  • Fovea contains most color cones

Color Perception

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  • The density of cones is not linear
  • Fovea contains most color cones

Color Perception

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  • Common color models
  • RGB
  • CMY
  • XYZ
  • HSV
  • HLS
  • etc…
  • Tristimulus / Trichomatic color theory

Color representation by computers

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  • Additive color model
  • Combining colors will produce white

RGB color model

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  • Subtractive color model
  • Combining colors will cover white, absorbing light

CMY color model

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Conversion: R=I-C, G=I-M, B=I-Y

RGB and CMY

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CMYK

  • CMY used in printing can be expensive
  • Black is cheap
  • K = Key or blacK
  • K = min(C,M,Y)
  • CMY amount is then reduced by K
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HSV

  • Hue, Saturation and

Value

  • More natural to manipulate for humans
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HSL

  • Hue, Saturation and Lightness
  • Maximum saturation is at L=0.5
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Gamma correction

  • Color intensities are considered linear
  • Most display devices are non-linear
  • Intensity(voltage) ~= 2 x Intensity(voltage / 2)

y = 𝑦1/𝛿

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Color Matching

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Color Matching

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Color Matching

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Color Matching Functions

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CIE XYZ colors

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CIE XYZ colors

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CIE XYZ colors

  • Commission Internationale de l’Éclarige 1931
  • Device independent, CIE 1931
  • Based on three standardized colors X,Y,Z

x = X/(X+Y+Z) y = Y/(X+Y+Z) z = Z/(X+Y+Z)

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CIE xyY chromaticity diagram

  • Defines color gamut of displays
  • Mapped on to 2D
  • x +y + z = 1
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RGB color gamut

  • Devices are usually not capable to display all colors
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Color representation in code

  • 32bit per channel
  • Normalized colors: floating point <0,1> for each channel
  • 8bit per channel
  • <0,255> range
  • OpenGL “GLUByte”
  • C++ uint8_t
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  • Image Filtering
  • Pixel operations
  • Filtering
  • Warping
  • Composition
  • Morphing
  • Image Manipulation
  • Sampling and Reconstruction
  • Quantization and Aliasing

Image Processing

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 Images

 Pixels are samples  Images are 2D arrays  Images have limited resolution

 Colors

 Visible light spectrum  Anatomy of eye and perception  Basic color models and their applications  CIE chromaticity diagram  Gamma correction

Summary

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  • Ask questions, please!!!
  • Be communicative
  • www.slido.com #PPGSO09
  • More active you are, the better for you!

How the lectures should look like #2

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Image Processing

Next Week

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Acknowledgements

 Thanks to all the people, whose work is shown here and whose

slides were used as a material for creation of these slides:

Matej Novotný, GSVM lectures at FMFI UK Peter Drahoš, PPGSO lectures at FIIT STU

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www.slido.com #PPGSO09 martin.madaras@stuba.sk

Questions ?!