CS 112 – Display Considerationsmajumder/VC/classes/display.pdf · Microsoft PowerPoint -...
Transcript of CS 112 – Display Considerationsmajumder/VC/classes/display.pdf · Microsoft PowerPoint -...
Aditi Majumder, CS 112 Slide 1
CS 112 – Display Considerations
Aditi Majumder, CS 112 Slide 2
Display
Image generationGenerate digital imagesShould take care to have non-aliased images
Image ReconstructionGenerate a continuous image on the display
Aditi Majumder, CS 112 Slide 3
Image Reconstruction
Each pixel is not a point but an areaHow is that area lighted?
2D 1D
Ideal CaseIn practice
Spot WidthWider spots Narrower spots
Aditi Majumder, CS 112 Slide 4
Sampling
f s 2
-f s 2
f s 2 f s 3 f s - f s
f s 2 f s 3 f s - f s
Convolution
Aditi Majumder, CS 112 Slide 5
Reconstruction
f s 2
-f s 2
f s 2 f s 3 f s - f s
f s 2
-f s 2
Multiplication
Aditi Majumder, CS 112 Slide 6
Reconstruction (Wider Kernel)
f s 2
-f s 2
f s 2 f s 3 f s - f s
f s 2
-f s 2
PIXELIZATION: Lower frequency aliased as high frequency
Aditi Majumder, CS 112 Slide 7
Reconstruction (Narrower Kernel)
f s 2
-f s 2
f s 2 f s 3 f s - f s
f s 2
-f s 2
BLURRING: Removal of high frequencies
Aditi Majumder, CS 112 Slide 8
Aliasing artifacts (Right Width)
Aditi Majumder, CS 112 Slide 9
Wider Spots (Lost high frequencies)
Aditi Majumder, CS 112 Slide 10
Narrow Width (Jaggies, insufficient sampling)
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Quantization
Digitization of colorGray scale – infinite grays between 0 and 1
8 bit representation – 256 levelsA range of grays represented by a single value
Any value is assigned to one of k valuesChoose number of levels and range of each level
Aditi Majumder, CS 112 Slide 12
Quantization Error
g
Q(g)
Quantization Error
Uniform Quantization
Maximum Error = ½ Step Size
Step Size
Aditi Majumder, CS 112 Slide 13
Human Perception
Use properties of human perceptionResponse CompressionResponse Expansion
Aditi Majumder, CS 112 Slide 14
Steven’s Power Law
P = KS n
P = PerceptionS = Stimulus
Strength
n>1.0 (Expansion)
n<1.0 (Compression)
Aditi Majumder, CS 112 Slide 15
Steven’s Power Law
P = KS n
P = PerceptionS = Stimulus
Strength
n>1.0 (Expansion)
n<1.0 (Compression)
Aditi Majumder, CS 112 Slide 16
Gamma Function
Inverse of human response curve for faithful representation of intensitiesCalled the gamma function
O = Iγ
Gamma Correction
Human Response
Display Gamma
Input Intensity
Out
put I
nten
sity
Aditi Majumder, CS 112 Slide 17
Capture devices (Camera)
Usually have the inverse gammeSimilar to human eye
So that images look good on displayCurrent cameras give you RAW images which are linear
Aditi Majumder, CS 112 Slide 18
Non-Uniform Quantization
Note how quantization changesNon-uniform step sizeMaximum Error
½ of maximum step size
# of levels is the color resolution
# of bits
Display Gamma
Input Intensity
Out
put I
nten
sity
Aditi Majumder, CS 112 Slide 19
Color Resolution
Analog Image 4 Steps 8 Steps 16 Steps
32 Steps64 Steps
Quantization Artifacts
Aditi Majumder, CS 112 Slide 20
Dithering
What if the color resolution is low?Newsprint – Bi-level, only black and white
Can we expand the # of colors?Spatial integration of eye
Trading off spatial resolution for intensity resolution
Aditi Majumder, CS 112 Slide 21
Dithering
Represented by a dither matrixnxn pixels, bi-level intensity, can produce n2+1 intensitiesIf more than two levels – k levels
n2. (k-1) +1Used for increasing the color resolution
0 2
1 3
Aditi Majumder, CS 112 Slide 22
Dithering
If more than two levels – k levelsn2. (k-1) +1For k = 4 (0,1,2,3) and n=2
Aditi Majumder, CS 112 Slide 23
Examples
Loss of tone and details(Intensity and Spatial Resolution)