Post on 17-Oct-2021
Digital Images
ENGG1015
1st Semester, 2010
Dr. Hayden Kwok-Hay So
Department of Electrical and Electronic Engineering
Back to top-level
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Applications
Systems
Digital Logic
Circuits
Electrical Signals
High Level
Low Level
• Computer & Embedded Systems • Computer Network • Mobile Network
• Image & Video Processing
• Combinational Logic • Boolean Algebra
• Basic Circuit Theory
• Voltage, Current • Power & Energy
This week
Back to top-level
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Applications
Systems
Digital Logic
Circuits
Electrical Signals
High Level
Low Level
• Computer & Embedded Systems • Computer Network • Mobile Network
• Image & Video Processing
• Combinational Logic • Boolean Algebra
• Basic Circuit Theory
• Voltage, Current • Power & Energy
This week
Digital Images
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Representation
Processing Hardware
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Representing Images
bitmap
R G B
pixel
An image is broken down into small regions called picture elements (pixels)
Bitmap: A pixel-by-pixel representation of image
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Image Dimensions Image Size
• The number of pixel in X-Y direction • Sometimes quoted using the total number of pixels in a
picture (N megapixels)
Image Resolution • The density of pixels • Measured by pixel-per-inch (PPI)
15
14
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Representing Pixels Each pixel is represented by one or more
values Black & white images:
• Each pixel is represented by exactly 1 value (B or W)
• 1 bit is enough to represent 2 possible values Grey scale images:
• Each pixel is usually a byte, keeping the brightness or gray levels
Color images: • Each pixel represented a group of color
components of that location • Different color systems: RGB, CYMK, YCbCr, etc
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Monochrome and Gray-scale Images
Monochrome Image
Each pixel is 1 bit, either 0 or 1
Dithering is used to produce different intensities
Gray-scale Image
Each pixel is usually a byte (8-bit), keeping the brightness or gray levels
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B&W B&W (w/ dither) Grayscale
Color Images
indexed color image # of color support
depends on the # of bit for each pixel • 4 bits 16 colors • 8 bits 256 colors
Color Look-Up Tables (LUTs)
Color palette
24-bit color image Each pixel is
represented by 3 bytes using a certain color model
Supports 256x256x256 colors • 16 million colors
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16 colors 256 colors 16M colors
RGB Color Model Additive color model Primary colors: Red, Green,
and Blue Secondary colors obtained by
additive mixing of primary colors
Commission Internationale d'Eclairage (CIE) specifies red to be 700nm, green to be 546.1nm and blue to be 435.8nm
Used in media that transmit light (e.g. TV)
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CMY Color Model Subtractive color model Subtractive primaries:
Cyan, magenta, and yellow
A subtractive primary absorbs a primary color and reflects the other two • E.g. Cyan absorbs red and
reflect blues and green
Used in printing device
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Printing an Image Print Size
• Depends on the mapping between printer’s resolution, image resolution & image size
• A Printer’s printing resolution is usually higher than an image’s resolution because multiple dots of ink are needed to created color of an image pixel
Color Space • On screen display: RGB (additive) • Printing devices: CMYK (subtractive)
Color Production • Each pixel may have different color • Each ink drop has only 1 color
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Dithering Create the illusion of new colors and shades by
varying the pattern of dots. • E.g. Newspaper photographs are dithered. If you
look closely, you can see that different shades of gray are produced by varying the patterns of black and white dots. There are no gray dots at all.
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Dither, Halftone, Grayscale
original dither halftone
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RGB Color Space
The RGB model describes the formation of color by mixing different portion of Red, Blue and Green light.
But what is “red”, “blue” and “green”? • E.g. which of the colors on top of this page is
“red”? A color space defines objectively the exact
color that is represented numerically so the same information may be reproduced on different machines.
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sRGB color space Originally defined by HP and Microsoft Now the de facto standard on the Internet
and most consumer electornics • Digital camera, HDTV, computer monitors, etc
If a color profile is not specified, the default assumption is that the colors are specified in sRGB color space
Given the specification of the 3 primary colors (R, G, B), the colors representable will be the color triangle spanned from the three colors.
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Common Color Spaces
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More Color Models Both RGB and CMY(K) model specify how to
form a color But they have little resemblance to how human
beings reason about colors E.g. How do you get the RGB values of the
pale orange color on the right?
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[R G B] = [204 131 42]
[R G B] = [? ? ?]
[248 215 152]
HS(B/V), HSL, HSI Color Model The family of HSx models describe colors
similar to how human perceives colors • Also similar to how painters create colors
HSB: Hue Saturation Brightness HSV: Hue Saturation Value HSL: Hue Saturation Lightness HSI: Hue Saturation Intensity Similar, but often comes with confusing (or
even contradicting) definitions
Cylindrical-Coordination Hue:
• The dominant color • The angle away
from red Saturation
• The amount away from the center
• How “full” the color is
Lightness/Brightness/Value • The amount of
white/black added
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More Image Representations? Raster image (bitmap image) - Raster
graphics uses pixel values to describe an image. The file size is independent of the image complexity. For higher resolution, the file size increases dramatically
Vector graphics (draw graphics) - An alternate approach is to use only instructions for drawing lines, circles, ellipses, curves, and other shapes.
Vector Graphics Vector-based images are composed of
key points and paths which define shapes, and coloring instructions, such as line and fill colors.
Example:
Vector Graphics Advantages Vector graphics can be scaled up and down
easily and quickly while retaining the quality of the picture. Raster images scale poorly and display poorly at resolutions other than that for which the image was originally created.
Vector graphics require less bandwidth and can be accessed and viewed faster than raster graphics.
Vector graphics can be edited and manipulated far easier than raster images.
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Image Processing
Used in digital camera, TV, cell phones…
Used in all kinds of photo editing SW • e.g. Photoshop, GIMP…
Image Processing - Examples
Original Greyscale
Blur Edge Detection
RGB to Grey-scale Conversion Each pixel of a grey-scale image has only
one intensity value, V
High V: white, Low V: black
Easiest conversion:
Produce better result if you weight G and R more than B • Human eyes are more sensitive to green and red €
V =R +G + B
3
Basic Filtering: Matrix Convolution Filters are building blocks of image processing
systems One of the most basic filtering method is by matrix
convolution
€
y[r,c] =1h[i, j]
i, j∑h[ j,i]x[r − j,c − i]
i=−1
1
∑j=−1
1
∑r
c
Matrix Convolution in Action
12 8 27 26 54 48 14 9 16 8 29 9 3 11 10 15 50 60 8 12 34 2 29 52
17 2 44 35 56 72 22 39 43 34 63 77
1 2 1 2 4 2 1 2 1
9 16 8 11 10 15 12 34 2
1 × + 2 × + 1 × 2 × + 4 × + 2 × 1 × + 2 × + 1 ×
+ + = 14
14 19
16 8 29 10 15 50 34 2 29
19
X H Y
34
34 8 29 9 15 50 60 2 29 52
Gaussian Blur A simple but effective way to blur a picture Each pixel is replaced with a weighted sum of the
values of its surrounding pixels The weighting factors have a Gaussian distribution,
thereby the name Intuitively: each pixel is mixed to certain extent with
its neighbors
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2 4 5 4 2
4 9 12 9 4
5 12 15 12 5
4 9 12 9 4
2 4 5 4 2
Edge Detection Useful in understanding an image
• For robot, face recognition, medical imaging etc
In a smooth contour, the pixel values usually do not change rapidly
However, the pixel exhibit sudden jump in values near an edge • E.g. jump from 1 to 130
Sobel edge detection is one of the simplest algorithms that makes use of this observation to find edges • Compares values of the neighbors of pixel
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Sobel filter
Sobel filter use the results of two filters • In x and y direction
Magnitude of the resulting pixel as:
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-1 0 +1
-2 0 +2
-1 0 +1
Gx
+1 +2 +1
0 0 0
-1 -2 -1
Gy
€
G = Gx2 +Gy
2
G ≈ Gx + Gy Easier to compute
Sobel Filter Example – x Dir
3 3 3 39 39 39 3 3 3 40 40 40 3 3 3 41 41 41 3 3 3 42 42 42 3 3 38 41 41 41 3 3 37 40 40 40
-1 0 1 -2 0 2 -1 0 1
3 3 3 3 3 3 3 3 3
-1 × + 0 × + 1 × -2 × + 0 × + 2 × -1 × + 0 × + 1 ×
+ + = 0
0 152
3 3 40 3 3 41 3 3 42
152
X H Y
152
152 3 40 40 3 41 41 3 42 42
Sobel Filter Example – x Dir
3 3 3 39 39 39 3 3 3 40 40 40 3 3 3 41 41 41 3 3 3 42 42 42 3 3 38 41 41 41 3 3 37 40 40 40
-1 0 1 -2 0 2 -1 0 1
X H Y 0 0 148 148 0 0
0 0 148 148 0 0
0 0 152 152 0 0
0 0 154 154 0 0
0 0 152 152 0 0
0 0 152 152 0 0
Image Processing Summary Image processing is the task of
manipulating the image by mathematical means to achieve high level requirements
Common operations: filtering Many other operations: E.g. Image forensic, Lithography,
medical imaging, automatic image diagnosis, robot control, etc…
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Digital Cameras Resolution measured in pixels H x V
Image sensing: charge coupled device (CCD)
Megapixels is used to denote the total max pixels in the image • E.g. 5 Megapixel - in the 2520 by 1890 and higher
pixel range. Photo quality 11 x 14 prints from this class of camera.
Comparing film cameras to digital cameras is difficult since resolution is measured differently
Taking Pictures 1. Image captured by lens 2. Image focuses on CCD 3. CCD generates analog
representation of image 4. Analog signal converts to
digital 5. Digital signal processing
(DSP) adjust quality, etc Step 5
Step 4
Step 3
Step 1
Step 2
Marketing Caveats Q: For digital cameras, higher
“megapixel” value always produce better photos?
A: Not really. If you will only look at the photos on websites, or will only print them on 3R papers, you don’t need all the pixels from a 10M pixels camera.
Area You Ready?
Flat Panel TVs and Monitors Pictures displayed as matrix of pixels on screen Two major technologies for generating picture
• Plasma • Liquid Crystal Display (LCD)
Plasma • Neon-Xenon gas trapped between two glasses • When electrically charged, each pixel display red,
blue or green color. LCD
• Liquid crystal between glasses pass/block light depending on electrical signal
• Pass corresponding backlight
LED TVs? Misleading term
Proper name: LED-backlight LCD TVs
Use the same LCD display technology as all other “LCD displays”.
Most other standard “LCD displays” use cold cathode fluorescent light (CCFL) for backlight
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Three Characteristic Dimensions Panel Size • The physical dimension of the panel • A 42” panel has a diagonal measurement of
42” Display Resolution • The number of picture-elements (pixels) along
each X-Y direction Dot Pitch • The distance between two pixel of the screen
Panel Size = Display Resolution * Dot Pitch
Standard Display Resolutions
Marketing Caveats Q: For flat panel TVs, a bigger screen
always produce better display than a smaller screen?
A: Not really. It depends on the distance you will be watching the TV and the TV source signal.
More Pixel = Good? Human eye can identify 120 pixels per degree
of visual arc • i.e. if 2 dots are closer than 1/120 degree, then our
eyes cannot tell the difference At a distance of 2m (normal distance to a TV)
our eyes cannot differentiate 2 dots 0.4mm apart.
Closer to TV => easier to differentiate pixels Far away => cannot tell the difference
screen
Minimum: 2 arc minute
Image courtesy of www.carltonbale.com
Source: http://www.diamond-vision.com/quad_dot_pattern.asp
True LED displays Each pixel is a
LED
Used mostly in outdoor, large- scale displays
Dallas Cowboys Stadium Sideline Display 48.64m x 21.76m Pixel Pitch: 20mm Displays World’s Largest High-Definition Video Display
Hong Kong Shatin Racecourse 70.4m x 8m World’s Longest TV screen
In Conclusion… Digital signal processing is a very broad
field within EEE The processing of digital image is a
good example of high-level applications that run on digital signal processing systems.
To display and process digital images correctly, you need the right combination of image representation, hardware, and processing power.
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