Image Processing and Sampling

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6 Νοε 2013 (πριν από 3 χρόνια και 7 μήνες)

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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