Computer Graphics - PowerPoint PPT Presentation

1 / 39
About This Presentation
Title:

Computer Graphics

Description:

Every periodic function can be represented as the sum of sine and cosine functions ... can be represented as sum of sine waves that are integer multiple of fundamental ... – PowerPoint PPT presentation

Number of Views:39
Avg rating:3.0/5.0
Slides: 40
Provided by: mrl8
Category:

less

Transcript and Presenter's Notes

Title: Computer Graphics


1
Computer Graphics
  • Recitation 7

2
Motivation Image compression
What linear combination of 8x8 basis signals
produces an 8x8 block in the image?
3
The plan today
  • Fourier Transform (FT).
  • Discrete Cosine Transform (DCT).

4
What is a transformation?
  • Function rule that tells how to obtain result y
    given some input x
  • Transformation rule that tells how to obtain a
    function G(f) from another function g(t)

5
What do we need transformations for?
  • Mathematical tool to solve problems
  • Change a quantity to another form that might
    exhibit useful features
  • Example
  • XCVI x XII ? 96 x 12 1152 ? MCLII

6
Periodic function
  • Definition g(t) is periodic if exists P such
    that g(tP) g(t)
  • Period of a function smallest constant P that
    satisfies g(tP) g(t)

7
Attributes of periodic function
  • Amplitude max value it has in any period
  • Period P
  • Frequency 1/P, cycles per second,Hz
  • Phase position of function within a period

8
Time and Frequency
  • example g(t) sin(2pft) (1/3)sin(2p(3f)t)

9
Time and Frequency
  • example g(t) sin(2pft) (1/3)sin(2p(3f)t)



10
Time and Frequency
  • example g(t) sin(2pft) (1/3)sin(2p(3f)t)



11
Time and Frequency
1, -a/2 lt t lt a/2 0, elsewhere
  • example g(t)

12
Time and Frequency
1, -a/2 lt t lt a/2 0, elsewhere
  • example g(t)




13
Time and Frequency
1, -a/2 lt t lt a/2 0, elsewhere
  • example g(t)




14
Time and Frequency
1, -a/2 lt t lt a/2 0, elsewhere
  • example g(t)




15
Time and Frequency
1, -a/2 lt t lt a/2 0, elsewhere
  • example g(t)




16
Time and Frequency
1, -a/2 lt t lt a/2 0, elsewhere
  • example g(t)




17
Time and Frequency
1, -a/2 lt t lt a/2 0, elsewhere
  • example g(t)


A (1/k)sin(2pkft)
18
Time and Frequency
  • If the shape of the function is far from regular
    wave its Fourier expansion will include infinite
    num of freq.

A (1/k)sin(2pkft)

19
Frequency domain
  • Spectrum of freq. domain range of freq.
  • Bandwidth of freq. domain width of the
    spectrum
  • DC component (direct current) component of zero
    freq.
  • AC all others

20
Fourier transform
  • Every periodic function can be represented as
    the sum of sine and cosine functions
  • Transform a function between a time and freq.
    domain

G(f) g(t)cos(2pft) - i sin(2pft)dt
g(t) G(f)cos(2pft) i sin(2pft)df
21
Fourier transform
  • Discrete

G(f) (1/n) g(t)cos(2pft/n) - i
sin(2pft/n) , 0ltfltn-1
g(t) (1/n) G(f)cos(2pft/n) i
sin(2pft/n) , 0lttltn-1
22
FT for digitized image
  • Each pixel Pxy point in 3D (z coordinate is
    value of color/gray level
  • Each coefficient describes the 2D sinusoidal
    function needed to reconstruct the surface
  • In typical image neighboring pixels have
    close values ? surface almost flat ?
    most FT coefficients small

23
Sampling theory
  • Image continuous signal of intensity function
    i(t)
  • Sampling store a finite sequence in memory
    i(1)i(n)
  • The bigger the sample, the better the quality?
    not necessarily

24
Sampling theory
  • We can sample an image and reconstruct it
    without loss of quality if we can
  • - Transform i(t) function from time to freq.
    Domain
  • - Find the max freq. fm
  • - Sample i(t) at rate gt 2fm
  • - Store the sampled values in a bitmap

25
Sampling theory
  • Some loss of image quality because
  • - fm can be infinite choose a value s.t freq. gt
    fm do not contribute much (low amplitudes)
  • - Bitmap may be too small
  • 2fm is Nyquist rate

26
Fourier Transform
  • Periodic function can be represented as sum of
    sine waves that are integer multiple of
    fundamental (basis) frequencies
  • Freq. domain can be applied to a non periodic
    function if it is nonzero over a finite range

27
Discrete Cosine Transform
  • A variant of discrete Fourier transform
  • - Real numbers
  • - Fast implementation
  • -Separable (row/column)

28
Discrete Cosine Transform
  • Example DCT on 8 points

f0 1 f17
G (½) C P cos((2t1)fp/16) , C
f
t
f
f
  • Fourier transform on 8 points

G P cos(2pft/8) i P sin(2pft/8) ,
f0,1,,7
t
f
t
29
Discrete Cosine Transform
  • Example 8 points
  • Same meaning the 8 numbers Gf tell what
    sinusoidal func. should be combined to
    approximate the function described by the 8
    original numbers Pt

30
Discrete Cosine Transform
  • Example 8 points

f0 1 f17
G (½) C P cos((2t1)fp/16) , C
f
t
f
f
  • G3 contribution of sinusoidal with freq.
    3tp/16 to the 8 numbers Pt
  • G7 contribution of sinusoidal with freq.
    7tp/16 to the 8 numbers Pt

31
Discrete Cosine Transform
  • Example 8 points

The inverse DCT
P (½) C G cos((2t1)jp/16) ,
t0,1,,7
j
t
j
32
Discrete Cosine Transform
  • 2D DCT

G C C Pxy cos((2x1)ip/2n)cos((2y
1)jp/2n)
j
i
ij
  • 2D Inverse DCT (IDCT)

P ¼ C C Gij cos((2x1)ip/16)
cos((2y1)jp/16)
xy
j
i
C f
f0 1 f17
33
Using DCT in JPEG
  • DCT on 8x8 blocks

34
Using DCT in JPEG
  • Block size
  • small block
  • - faster
  • - correlation exists between neighboring
    pixels
  • large block
  • - better compression in flat regions
  • Power of 2 for fast implementation

35
Using DCT in JPEG
  • DCT basis

36
Using DCT in JPEG
  • For almost flat surface most Gij0
  • For surface that oscillates much many Gij non
    zero
  • G00 DC coefficient
  • Numbers at top left of Gij contribution of low
    freq. sinusoidal to the surface, bottom right
    high freq.

37
Using DCT in JPEG
  • Numbers at top left of Gij contribution of low
    freq. sinusoidal to the surface, bottom right
    high freq.
  • Scan each block in zig-zag order

38
Image compression using DCT
  • DCT enables image compression by concentrating
    most image information in the low frequencies
  • Loose unimportant image info buy cut Gij at
    right bottom
  • Decoder computes the inverse IDCT

39
See you next time
Write a Comment
User Comments (0)
About PowerShow.com