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ELEC 1020
Media Production: Technology and Design
Lecture 3
Prof. James She (james.she@ust.hk)
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Announcement – 1 (Typhoon gone!)
1. Make-up labs for Session 3’s
students (+ new students)
•
•
•
6:30–8:30p, Wed. (today, right after
the lecture)
4:00–6:00p, Tomorrow
same venue - 3119 in Lab. building
2. Lab #2 next Mon. @ 3119
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Announcement – 2 (Next lecture)
Guest Speaker:
• Carmen Ng, Creative Lead @ JM Network
Topics:
• Difference between Design and Art
• Outdoor Media Advertising
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Last lectures
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What’s Media? Content and Medium
Digital Billboard
Network
Medium
1. Digital display
2. Internet
3.wireless
Content
1. REMAX
2. Sales Performance
3. Contact
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Last lectures
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Outcomes from this lecture
1. Vision and digital image
2. Digital Image Processing
3. Crowd Accelerated Innovation
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Image Representation
Analog (physical) form
- Intensity of light reflected on painting, photograph, film-based negative, etc.
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Given a digital display
How we could see an image like the analog
(physical) form?
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How does your eye works?
All about lights intensity…
http://www.youtube.com/watch?v=LpjbOhtcD0A&playnext=1&list=PL03E688C16
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96F94F4&feature=results_main
Digital Image Representation
Simulating the physicals
Amplitude domain: the light intensity at a
spatial location represented by a number
Spatial domain: a sequence of numbers
recorded to represent light intensity at a
grid of spatial locations
(i.e., pixels on a display)
0
12
100
0
86
255
100
28
255
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Pixel The basic unit that form an
image on a display
0
12
100
0
86
255
100
28
255
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Pixel
The basic unit of an image
a pixel
•
Any digital image on a display is
represented by a set of pixels
3 pixels
• Pixel not necessary to be a square;
 Image not necessary to be regular shape
•
4 pixels
Number of pixels in a given size of image
or display
 defining resolution
vs
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Pixel vs Resolution
Given a display area
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Resolutions
&
Pixels
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Resolution vs An image
More the better on the same size
higher resolution
lower resolution
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Full High Definition (1920 x 1080)
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Pixel (for B/W or gray-scale image)
Define a pixel by light intensity (through # of bits)
• A pixel is representing a value of light intensity:
1 bit: “0”=black, “1”=white;
8 bits: “0”=black, “255”=white;
• Differences between using?
1) 2-bit and 8-bit per pixel
2) 8-bit and 14-bit per pixel
•
Which case is worth putting 6 additional
bits?
•
When to invest more bits on a pixel to
enrich the digital representation of an
image?
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Bitmap - 1
Bitmap representation (Gray-scale)
– An image is formed by pixels
– Some computer memory is allocated to each pixel to store the
intensity of that pixel
– e.g., 3-bit gray scale bitmap representation (how many level?)
6
4
4
3
1
6
1
2
5
2
6
3
1
2
3
2
4
5
1
4
1
6
7
3
5
– Painting tools using bitmap: Photoshop, Microsoft Paint, …
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Bitmap - 2
Gray scale image
– Common natural images: 8 bits
– e.g., 8 bit, “0”=black, “255”=white
– Medical images: 12 bits
– Adjacent pixels are typically similar, except across edges and in
texture regions
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Bitmap - 3
Bitmap representation (color)
For each pixel:
– 3 primary colors: red, green,
blue
– 3 color values (3 memory
allocations)
– 8 bits for each color value
• 24 bits or 3 bytes per pixel
• 256 x 256 x 256 
16,777,216 different colors
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Bitmap - 4
24-bit RGB bitmap image
• Each color component forms a plane
• e.g.,
150 212
12
23
9
4
11
4
12
3
234 12
1
202 235
3
44 241 5 3
4 32 41
1
2
180 200
3
5 52
2
1
24
1
150
2 13
51 2
4
4
4
2
3
4
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0
23
191 212
0
113
3
191
41
4
124
5
127
77
1
90
4
87
19
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Combines to
form pixel color
88
125
B plane
G plane
R plane
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Outcomes from this lecture
1. Vision and digital image
2. Digital Image Processing
3. Crowd Accelerated Innovation
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Layers for Image Editing/
Processing
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Layers in Editing Tools
• Layers: like a stack of acetate sheet (i.e., the
transparent slide)
• Each layer: stores parts of the final image (objects,
effects, details, etc.)
• Editing operation on pixels across layers could be:
1) independent; 2) interrelated
• Editing/processing tools using layers: Photoshop
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Layers – independent operations
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Digital Image Processing
Alpha channel (recall your lab1)
– white acts as the visible area;
– black acts as the transparent area
– white area: allow you to see the background behind the image when
displayed
The 8-bit alpha = 255 (i.e., 11111111)
The 8-bit alpha = 0
(i.e., 00000000)
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Digital Image Processing
Example use of Alpha channel - 1
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Digital Image Processing
Alpha channel (an operational mask)
– 8-bit `gray scale’ alpha channel plane represents the degree of transparency
– Level of gray in between determines the level of visibility.
e.g., 50% gray  50 percent visibility.
– usually used with 16.8M color RGB images. The resulting image is called RGBA
(RGB+A, A means alpha channel).
i.e., 24-bit color + 8-bit alpha channel  32 bits per pixel
The 8-bit alpha = 255 (i.e., 11111111)
The 8-bit alpha = 0
(i.e., 00000000)
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Digital Image Processing
Example use of Alpha channel - 2
Image 1
Image 2
a channel
i.e., Image 1 x (255 – a)/255+ Image 2 x a/255
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Layers – interrelated operations
layer 1
Gray values
layer 2
1
2
7
8
3
4
9
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Result
layer 1 x (255 – a)/255+ layer 2 x a/255
Alpha value (a)
255
128
64
0
7
5
4.5
4
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Other Practical
Color Representations
• any other representation beside
RGB?
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Representation of color
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Other Parameters of
Color Representations
• Brightness Value (V)
– represents brightness of a color, from 0 to 100%.
• Hue (H)
– represents color, from 0 degrees to 360 degrees.
• Saturation (S)
– represents the gray-scale of a color space, from 0
to 100%.
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Brightness Value
• 𝑉 = max(𝑅, 𝐺, 𝐵)
R:
G:
B:
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32
64
H: 240
S: 50%
V: 25%
64
64
128
96
96
192
128
128
255
240
50%
50%
240
50%
75%
240
50%
100%
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Hue
Ordering
Formula
http://www.csgnetwork.com/csgcolorsel4.html
R: 64
G: 64
B: 128
R: 64
G: 128
B: 64
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Hue
Ordering
Formula
http://www.csgnetwork.com/csgcolorsel4.html
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Saturation
–𝑆= 1−
min 𝑅,𝐺,𝐵
𝑉
∗ 100%
R: 96
G: 96
B: 128
64
64
128
32
32
128
0
0
128
H: 240
S: 25%
V: 50%
240
50%
50%
240
75%
50%
240
100%
50%
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Image Perception and HSV
Contrast
The differences of V across pixels
•
Human eyes: more sensitive to the contrast, rather than absolute colors of
pixels in an image
•
Contrast = the differences of V values across pixels in an image
•
The bigger the contrast seems looking “nicer”!
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V values
V values
V values
Photoshop Curves
• Adjust Vs for a better perception of an image
• Mapping the corresponding RGB values automatically
Output
Input
1
2
5
8
Output value
1
1
4
8
Input value
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Photoshop Curves
• Improving image by increasing the contrast
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Value in HSV
Value in HSV
Photoshop Curves for keying
However,…
+
The range of the image is different
=
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Photoshop Curves
• Changing the value to match the background
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Photoshop Curves for keying
Plus other adjustments…
+
=
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More practical uses in
Photoshop
1. http://www.youtube.com/watch?v=ORM6AZY19pw
2. http://www.youtube.com/watch?v=N-TG0-qpZvs
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Outcomes from this lecture
1. Vision and digital image
2. Digital Image Processing
3. Crowd Accelerated Innovation
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Crowd Accelerated Innovation
(for arts, knowledge, designs, etc.)
PS: Check this if time allows: http://www.youtube.com/watch?v=X6Zo53M0lcY
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Crowd Accelerated Innovation
a. http://www.youtube.com/watch?v=D7o7BrlbaDs&feature=related
b. http://www.youtube.com/watch?v=zyLX2cke-Lw&feature=relmfu
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It also work on images!
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Crowd Accelerated Mosaic Arts
• Combining existing art pieces from others
to form a new one from yourself!
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Mosaic Arts
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Crowd Accelerated Innovation
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Crowd Accelerated Innovation
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Crowd Accelerated Innovation
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Crowd Accelerated Innovation
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- End of Lecture 2 -
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