Multimedia System ( Image & Graphics)

1. IMAGES AND GRAPHICS

2. A digital image is a representation of a two-
dimensional image as a finite set of digital values,
called picture elements or pixels

3. •Pixel values typically represent gray levels, colours,
heights, opacities etc
•Remember digitization implies that a digital image
is an approximation of a real scene
Pixel
1 pixel

4. Image Format
•Common image formats include:
▫ 1 sample per point (B&W or Grayscale)
▫ 3 samples per point (Red, Green, and Blue)
▫ 4 samples per point (Red, Green, Blue, and “Alpha”,
a.k.a. Opacity)
•For most of this course we will focus on grey-scale
images

5. Image Format
Bitmap:
An array of information that contains the
information for the image.
It is a 3 dimensional array
Width x Height x 24 (8 for each color)
So can be huge
(.bmp and .tif or .tiff are most common bitmaps)
JPG (Joint-Photographic Experts Group)
•Generally better for images and photos
•Spatial not color compression, can distort image spatially and more
loss with each save
•Now can animate as well.
•For continuous tone images, such as full-color photographs
•Supports more than 16 millions of color (24-bit)
•Uses lossy compression (averaging may lose information)

6. Image Format
GIF (Graphical Interchange Format)
For large areas of the same color and a moderate level of
detail.
Supports up to 256 colors
Allows transparency and interlacing
Uses lossless compression
PNG (Portable Network Graphic)
lossless, portable, well-compressed storage of raster images
patent-free replacement for GIF
also replace many common uses of TIFF
Support indexed-color, grayscale, and true color images + an
optional alpha channel for transparency

7. Image Format
• Monochrome just requires one bit per
pixel, representing black or white
BMP – 16 KB
• 8 bits per pixel allows 256 distinct colors
BMP – 119KB
• 16 bits per pixel represents 32K distinct colors (Most
graphic chipsets now supports the full 65536 colors
and the color green uses the extra one bit)
BMP – 234 KB
• 24 bits per pixel allows millions of colors
• 32 bits per pixel – trillion of colors
BMP – 350KB

8. Bitmapped vs. JPEG File Sizes
Both images are the same relative size.
900kb
.JPEG High Quality ~700kb

9. Graphics
• Computer generated or drawn by you.
• Specified through graphics primitives (Lines,
Rectangle, circle etc) and their attribute (Line
style, width, color etc).
• Not represent by pixel matrix.
• You can directly manipulate the elements
because you drew them – Sprites
• Additional conversion is required for Draw
pixel matrix.

10. Graphics Vs Images
• Basic element
▫ Specified through graphics primitives (Lines,
Rectangle, circle etc) and their attribute (Line style,
width, color etc).
▫ Pixel
• Manipulation
▫ You can directly manipulate the elements because
you drew them – Sprites
▫ In an image, you can manipulate pixels but not
directly the elements. This has a great impact.
• Visible
▫ Additional conversion is required for Draw pixel
matrix.
▫ No Conversion required

11. Dynamic In Graphics
•Motion Dynamics
▫ Object can be moved and enabled with respect
to the stationary object.
▫ Both the object and the camera are moving.
•Update Dynamics
▫Change the shape, colour, or other properties of
the objects being viewed.

12. Graphics Hardware
• Architecture of Raster Display
Minimum refresh rate 60 Hz
is used to avoid flickering

13. Screen Mosaic Triad Arrangement

14. Interlaced Projection

15. Dithering
• Dithering
▫ If we view a very small area from a sufficiently large viewing
distance, our eyes average fine detail within the small area and
record only the overall intensity of the area.
▫ This phenomenon used in hardware display is known as dithering
▫ Color display with three bits per pixel: red, green, blue
▫ 2X2 pattern area
▫ It can produce 5X5X5 color combinations

16. Digital Image Processing
•Digital image processing focuses on two major
tasks
▫ Improvement of pictorial information for
human interpretation
▫ Processing of image data for storage,
transmission and representation for
autonomous machine perception
•Some argument about where image processing
ends and fields such as image analysis and
computer vision start

17. • Computer image processing
• Image synthesis (generation)
• Image analysis (recognition)
• Image synthesis
• Pictorial synthesis of real or imaginary objects
• Mainly graphics concern with synthesis
• Image analysis
• Recognition of models from pictures of 2D or 3D
objects
Digital Image Processing (DIP)

18. Digital Image Processing(DIP)
Low Level Process
Input: Image
Output: Image
Examples: Noise
removal, image
sharpening
Mid Level Process
Input: Image
Output: Attributes
Examples: Object
recognition,
segmentation
High Level Process
Input: Attributes
Output:
Understanding
Examples: Scene
understanding,
autonomous navigation
•The continuum from image processing to
computer vision can be broken up into low,
mid- and high-level processes

19. Digital Image Processing(DIP)
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression

20. Image Recognition Steps
Steps:
Formatting
Conditioning
Labeling
Grouping
Extracting
Matching

21. Image Recognition Steps
Conditioning
Suppresses noise
Background normalization
By suppressing uninteresting systematic or pattern
variations
Labeling
Informative pattern has structure with set of connected
pixels.
Region, edge
Grouping
The grouping operation identifies the events by collecting together or
identifying maximal connected sets of pixels participating in the same kind of
event.
Example: Edges are grouped into lines, is called line-fitting

22. Image Recognition Steps
Extracting
Extracting operation computes for each group of
pixels a list of properties.
Example:
Centroid
Area
Orientation
Spatial moments, gray tone moments, spatial-gray
tone moments
Circumscribing circle, inscribing circle,
Matching
Determines the interpretation of some related set of
image events, associating these events with some
given three-dimensional object or two-dimensional
shape.
Example: Template matching

23. Image Transmission
Raw Image Transmission
Size = spatial resolution X pixel quantization
Compressed image data transmission
JPEG, MPEG
Symbolic image data transformation
image primitive, attribute etc.