Image Processing and Sampling
Aaron Bloomfield
CS 445: Introduction to Graphics
Fall 2006
Overview
Image
representation
What
is
an
image?
Halftoning
and
dithering
Trade
spatial
resolution
for
intensity
resolution
Reduce
visual
artifacts
due
to
quantization
What is an Image?
An
image
is
a
2
D
rectilinear
array
of
pixels
Continuous image
Digital image
What is an Image?
An
image
is
a
2
D
rectilinear
array
of
pixels
Continuous image
Digital image
A pixel is a sample, not a little square!
What is an Image?
An
image
is
a
2
D
rectilinear
array
of
pixels
A pixel is a sample, not a little square!
Continuous image
Digital image
Image Acquisition
Pixels
are
samples
from
continuous
function
Photoreceptors
in
eye
CCD
cells
in
digital
camera
Rays
in
virtual
camera
Image Display
Re

create
continuous
function
from
samples
Example
:
cathode
ray
tube
Image is reconstructed
by displaying pixels
with finite area
(Gaussian)
Image Resolution
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
Width x Height
Depth
Rate
NTSC
640 x 480
8
30
Workstation
1280 x 1024
24
75
Film
3000 x 2000
12
24
Laser Printer
6600 x 5100
1

Typical
Resolutions
Sources of Error
Intensity
quantization
Not
enough
intensity
resolution
Spatial
aliasing
Not
enough
spatial
resolution
Temporal
aliasing
Not
enough
temporal
resolution
)
,
(
2
2
)
,
(
)
,
(
y
x
y
x
P
y
x
I
E
Overview
Image
representation
What
is
an
image?
Halftoning
and
dithering
Reduce
visual
artifacts
due
to
quantization
Quantization
Artifacts
due
to
limited
intensity
resolution
Frame
buffers
have
limited
number
of
bits
per
pixel
Physical
devices
have
limited
dynamic
range
P(x, y) = trunc(I(x, y) + 0.5)
I(x,y)
P(x,y)
P(x,y)
(2 bits per pixel)
I(x,y)
Uniform Quantization
Uniform Quantization
8 bits
4 bits
2 bits
1 bit
Images
with
decreasing
bits
per
pixel
:
256
bpp
32
bpp
8
bpp
2
bpp
Reducing Effects of Quantization
Halftoning
Classical
halftoning
Dithering
Error
diffusion
dither
Random
dither
Ordered
dither
Classical Halftoning
Use
dots
of
varying
size
to
represent
intensities
Area
of
dots
proportional
to
intensity
in
image
P(x,y)
I(x,y)
Classical Halftoning
Newspaper image from
North American Bridge Championships Bulletin
, Summer 2003
Halftone patterns
Use
cluster
of
pixels
to
represent
intensity
Trade
spatial
resolution
for
intensity
resolution
Figure 14.37 from H&B
Dithering
Distribute errors among
pixels
Exploit
spatial
integration
in
our
eye
Display
greater
range
of
perceptible
intensities
Uniform quantization
discards all errors
i
.
e
.
all
“rounding”
errors
Dithering
is
also
used
in
audio,
by
the
way
Floyd

Steinberg dithering
Any
“rounding”
errors
are
distributed
to
other
pixels
Specifically
to
the
pixels
below
and
to
the
right
7
/
16
of
the
error
to
the
pixel
to
the
right
3
/
16
of
the
error
to
the
pixel
to
the
lower
left
5
/
16
of
the
error
to
the
pixel
below
1
/
16
of
the
error
to
the
pixel
to
the
lower
right
Assume
the
1
in
the
middle
gets
“rounded”
to
0
00
.
0
00
.
0
00
.
0
00
.
0
00
.
1
00
.
0
00
.
0
00
.
0
00
.
0
0.00 0.00 0.00
0.00 0.00 0.44
0.19 0.31 0.06
Error Diffusion Dither
Spread
quantization
error
over
neighbor
pixels
Error
dispersed
to
pixels
right
and
below
Figure 14.42 from H&B
+
+
+
1.0
Floyd

Steinberg dithering
Floyd

Steinberg
dithering
is
a
specific
error
dithering
algorithm
Uniform
Quantization
(1 bit)
Floyd

Steinberg
Dither (1 bit)
Original
(8 bits)
Floyd

Steinberg
Dither (1 bit)
(pure B&W)
Random Dither
Randomize
quantization
errors
Errors
appear
as
noise
P(x, y) = trunc(I(x, y) + noise(x,y) + 0.5)
I(x,y)
P(x,y)
I(x,y)
P(x,y)
Random Dither
Uniform
Quantization
(1 bit)
Random
Dither
(1 bit)
Original
(8 bits)
Ordered Dither
Pseudo

random
quantization
errors
Matrix
stores
pattern
of
thresholds
i = x mod n
j = y mod n
e = I(x,y)

trunc(I(x,y))
if (e > D(i,j))
P(x,y) = ceil(I(x, y))
else
P(x,y) = floor(I(x,y))
2
0
1
3
2
D
Ordered Dither
Bayer’s
ordered
dither
matrices
Reflections
and
rotations
of
these
are
used
as
well
2
0
1
3
2
D
10
2
8
0
6
14
4
12
9
1
11
3
5
13
7
15
4
D
+
+
+
+
2
2
2
2
2
2
2
2
2
2
2
2
)
2
,
2
(
4
)
1
,
2
(
4
)
2
,
1
(
4
)
1
,
1
(
4
n
n
n
n
n
n
n
n
n
U
D
D
U
D
D
U
D
D
U
D
D
D
Ordered Dither
Uniform
Quantization
(1 bit)
4x4 Ordered
Dither
(1 bit)
Original
(8 bits)
Dither Comparison
Random
Dither
(1 bit)
Original
(8 bits)
Ordered
Dither
(1 bit)
Floyd

Steinberg
Dither
(1 bit)
Color dithering comparison
Original
image
Web

safe
palette,
no
dithering
Web

safe
palette,
FS
dithering
Optimized
256
color
palette
Optimized
16
color
palette
Optimized
16
color
palette
FS
dithering
No
dithering
FS
dithering
Summary
Image
representation
A
pixel
is
a
sample,
not
a
little
square
Images
have
limited
resolution
Halftoning
and
dithering
Reduce
visual
artifacts
due
to
quantization
Distribute
errors
among
pixels
Exploit
spatial
integration
in
our
eye
Sampling
and
reconstruction
Reduce
visual
artifacts
due
to
aliasing
Filter
to
avoid
undersampling
Blurring
is
better
than
aliasing
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