Computer Graphicssanja/ComputerGraphics2012/P02...Computer Graphics P02 Digital Images and Graphics...

Computer Graphics P02 Digital Images and Graphics Formats

Aleksandra Pizurica

Ghent University

Telecommunications and Information Processing

Image Processing and Interpretation Group

P02_2

Overview

• Digital images

Sampling and quantization

Image resolution

Computer screen standards

• Graphic formats

Vector graphics

Raster graphics

• Color perception and reproduction

Tristimulus theory of color perception

True color images

Indexed color images

Digital Images

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Digital Images (1)

Illustration from Computer Graphics course of Thomas Funkhouser, Princeton University

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Digital Images (2)


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Digital Images (3)


• Picture element … Pictel … Pixel

• Pixel is a sample (a single point in a raster image)

• In some contexts (raster graphics, printing), pixel is considered as a smallest controllable element of a displayed or printed image, and terms like “square” or a “rectangular” pixel of a given size are used E.g. for 300dpi “pixel size” is 1/300 of an inch (~84.67mm)

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Digital Images (4)


Digital Images (5)

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• How to represent a “black-and-white” photographic image?

• Light intensity is continuous

• It is quantized in a number of discrete „grey values‟

Each pixel has a given grey value (also called „pixel intensity‟)

Extreme grey values are black and white: typically 0 - black and 255 –

white

• The number of grey values (bit depth) depends on the application

• Human eye can distinguish approximately 100 grey tones

Various bith depths

Grey- values Bits

2 1

4 2

8 3

16 4

256 8

From computer graphics course of Patrick Bergmans

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Image Resolutions

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• Intensity resolution

Each pixel has only “Depth” bits for colors/intensities

• Spatial resolution

Image has only “Width” x “Height” pixels

• Temporal resolution

Monitor refreshes images at only “Rate” Hz

Slide from Computer Graphics course of Thomas Funkhouser, Princeton University

Computer screen standards

Name Pixels HxV Ratio Total pixels

• QVGA 320×240 04:03 76.800

• WQVGA 432×240 18:10 103.680

• HVGA 480×320 03:02 153.200

• EGA 640×350 09:05 224.000

• VGA 640×480 04:03 307.200

• WGA of WVGA 800×480 05:03 384.000

• SVGA 800×600 04:03 480.000

• XGA 1024×768 04:03 786.432

• XGA+ 1152×864 04:03 995.328

• SXGA 1280×1024 05:04 1.310.720

• WXGA1 1280×800 16:10 1.024.000

• WXGA2 1366×768 16:09 1.049.088

• WSXGA of WXGA+ 1440×900 16:10 1.296.000

• SXGA+ 1400×1050 04:03 1.470.000

• WSXGA 1600×1024 25:16 1.638.400

• WSXGA+ 1680×1050 16:10 1.764.000

• UXGA 1600×1200 04:03 1.920.000

• WUXGA 1920×1200 16:10 2.304.000

• QXGA 2048×1536 04:03 3.145.728

• WQXGA 2560×1600 16:10 4.096.000

• QSXGA 2560×2048 05:04 5.242.880

• QSXGA+ 2800×2100 04:03 5.880.000

• WQSXGA 3200×2048 25:16 6.553.600

• QUXGA 3200×2400 04:03 7.680.000

• WQUXGA 3840×2400 16:10 9.216.000

• HSXGA 5120×4096 05:04 20.971.520

• WHSXGA 6400×4096 25:16 26.214.400

• HUXGA 6400×4800 04:03 30.720.000

• WHUXGA 7680×4800 16:10 36.864.000

Common standards for

classic 4/3 screens

Common standards for

wide screens

From computer graphics course of Patrick Bergmans

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Binary representations (1)

• Floating point or fixed point

• Graphical algorithms usually employ integers

• Remember: with n bits 2n symbols can be represented

integers from 0 to 2n-1

integers from -2(n-1) to 2(n-1) -1

other combinations possible

210 = 1024 ~ 103 = “1k”

224 = 2(2*10+4) = 210 x 210 x 24 = 1k x 1k x 16 = 16 M(ega) 237 = 2(4*10-3) = 210 x 210 x 210 x 210 x 2-3 = 1/8 T(era) ~ 125 G(iga)

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Binary representations (2)

n 2**n

8 256

10 1,024 1 kilo

16 65,536

20 1,048,576 1 Mega

24 16,777,216

30 1,073,741,824 1 Giga

32 4,294,967,296

40 1,099,511,627,776 1 Tera

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Graphic formats

Vector and Raster Graphics

• There are two main types of computer images

Raster images

• Each image point separately stored

Vector images

• Image consists of objects

• Objects are represented by their contours

• Each of these formats has certain advantages and

disadvantages and they are generally used for different

purposes

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Vector graphics

• Represented in terms of vertices (points) and lines.

• Lines can be straight or curved; Generated mathematically

• Ideal for “simple” images with precise contours (cartoons, logo‟s,…)

• For displaying on Cathode-Ray Tube (CRT) screen, a vector generator

converts the digital coordinates to analog voltages for beam deflection

P02_16 Fig. 1.4 from book FvDFH

Ideal line drawing Vector scan

Raster outline

• Represented in terms of elementary picture elements: pixels

• Well suited for images with a variety of intensity levels, such as digital

photographs and other “realistic” images

• Commonly referred to as bitmap (BMP) even though bitmap strictly

speaking applies only to binary systems

Raster graphics

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Ideal line drawing Fig. 1.4 from book FvDFH

Raster scan with filled primitives

An example of a raster image

Illustration from computer graphics course of Patrick Bergmans

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Architecture of a raster display

Fig. 1.2 from book FvDFH

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The raster is stored as a matrix of pixels representing the screen area. The image is scanned sequentially (video controller) one line at a time. At each pixel, the beam intensity is adjusted according to pixel intensity.

Frame Buffer Refresh

Refresh rate typically 60-75Hz Fig. 1.3 from book FvDFH

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Vector Versus Raster Systems

•Advantages of raster systems

Richer textural and pattern representation

Cheaper (repetitive regular scanning compared to complex random

scan of vector systems)

• Advantage of vector systems

No jaggies

Scales up arbitrarily remaining

perfectly smooth lines

Easier real-time manipulation

Example from Wikipedia

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Color perception and reproduction

Color Perception (1)

• Color as such does not exist in nature!

(Colored) light consists of electromagnetic waves of different

frequencies (or wavelengths)

• Color perception is three-dimensional (3D)

We have three types of “sensors” in the eye, which are sensitive

to different parts of the spectrum (Tristimulus theory of color)

• All color representations are hence 3D

Additive (RGB) ; red-green-blue

Subtractive (CMY) ; cyan-magenta-yellow

Other (HSV, Luv, Lab)

• We limit ourselves to RGB representation

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Spectral-response functions of the three types of cones in the human retina.

Fig. 13.18 from book FvDFH


• Natural white light consists of an continuous and uniform

spectrum of EM waves, visible from violet to red

(rainbouw)

Ultra violet infrared

wavelength 400 nm 750 nm


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• Color perception depends only on how the light activated

each of the three sensors (Red, Green and Blue)

wavelength

wavelength

Note: Spectral responses here are only schematical (true ones on slide #24)


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• Two totally different spectra can be perceived as the

same color

wavelength

wavelength


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• Two totally different spectra can be perceived as the

same color

• To reproduce artificially (the impression of) a color, it is

sufficient to generate the same sensor stimuli

• It is best done with light sources with wavelengths that are

close to the maximum sensitivity of the sensors, hence

Red, Green, Blue called R, G, B primaries

• This process is called additive color synthesis

By mixing R, G, B primaries in the right proportions, we can create

impressions of all colors

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Color images (1)

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Color images (2)

• One bit per color (0/1) – 3 bits/pixel

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Color images (3)

• With 8 bits per color 24 bits per pixel

• In this way we can theoretically represent 16.777.216

different colors

• Human eye can differentiate “only” several hundreds of

thousands of colors

• The 24-bit representation is in principle inefficient

• However, much simpler than the “optimal” one

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Color images (4)

• Eight bits per color (0-255). 24 bits/pixel

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• 24 bits is too much for some applications

Especially when the image contains only a limited number of colors

(e.g., cartoon or logo)

• Solution: instead of representing a color with 24 bits give

as input the color “index” or number of this color

How many colors need to be displayed at a time? N

How many bits are needed to represent this number? n = log2(N)

For N=256, n=8

• For every pixel the color “index” or the color number is

saved with only n bits

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color 2 (20,150,200)

color 3 (0,200,150)

color 253 (200,0,20)

color 1 (120,60,80)

color 0 (200,100,0)

color 254 (255,255,255)

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• Color-look-up (CLT) table gives the relationschip

between the color index and the true color

RED GREEN BLUE

0 200 100 0

1 120 60 80

2 20 150 200

3 0 200 150

4 50 100 150

… … … …

… … … …

253 200 0 20

254 255 255 255

255 0 0 200

24 bits

N =

2n p

ositions

color nr. 0 color nr. 1 color nr. 2

etc.

color nr. 255 color nr. 254 color nr. 253

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Problems with CLT

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• It can be difficult to exchange raw image data and/or color map tables between different image files (intermediate mapping needed).

• If the original color palette for a given indexed image is lost, it can be nearly impossible to restore it.

A typical indexed 256-color image and a result of restoring it with a wrong color palette

[http://en.wikipedia.org/wiki/Indexed_color]

Summary

• Digital images are of finite resolution (intensity, spatial,

temporal)

• Vector and raster graphics have each their advantages in

certain applications

• By mixing R, G and B primaries in the right proportions

all colors can be reproduced

• Indexed color palettes interesting in many practical

applications, especially for „cartoon-like‟ images (with

piece-wise uniform colors) and logo‟s

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Computer Graphicssanja/ComputerGraphics2012/P02...Computer Graphics P02 Digital Images and Graphics...

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