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Digital Halftoning
Sasan Gooran
PhD Course May 2013
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DIGITAL IMAGES (pixel based)
Scanning
Photo Digital image
ppi (pixels per inch): Number of samples per inch
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ppi (pixels per inch)
• ppi (scanning resolution): Number of samples per inch
• The higher ppi the better the representation of the con-tone image (Photo)
• Higher ppi requires more memory • ppi should not be unncessarily high • Choice of ppi????
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ppi = 72
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ppi = 36
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ppi = 18
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DIGITAL IMAGES Memory
• Grayscale 8 256 tones
• RGB 3*8=24 256^3=16.7
bits/pixel
million colors
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DIGITAL HALFTONING
• Since most printing devices are not able to reproduce different shadows of gray the original digital image has to be transformed into an image containing white (0’s) and black (1’s)
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Halftoning
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DIGITAL HALFTONING
Prepress Halftoning Print Con-tone
Image Halftoned Image
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DIGITAL HALFTONING Example
Periodic and clustered dots (AM)
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DIGITAL HALFTONING Example
Non-periodic and dispersed dots (FM)
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HALFTONE CELL Pixel (/a number of pixels)
Halftone cell
The fractional area covered by the ink corresponds to the value of the pixel (or the area)
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HALFTONE CELL
Original image Halftoned image
Halftone cell
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SCREEN RULING/FREQUENCY
• lpi (lines per inch): Number of halftone cells per inch
• The higher lpi the better the print (?!) • High lpi requires more stable print press etc. • Does a higher lpi always lead to a better
print? (to be answered later)
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RULE OF THUMB
lpisizeOriginalsDppi *2* ize esired=
Ex. A 10 x 15 cm2 photo that is supposed to be 20 x 30 cm2
when printed at 150 lpi has to be scanned with a ppi about 2*2*150 = 600.
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HALFTONE CELL Micro dot
dpi: Number of micro dots per inch This halftone cell represents at most 82 + 1= 65 gray tones
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HALFTONE CELL
Halftone cell Resolution: number of micro dots per inch (dpi)
Micro dot
In this case: 17 gray tones
Screen ruling: number of halftone cells per inch (lpi)
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lpi & dpi
• lpi: Number of halftone cells per inch • A halftone cell consists of micro dots • dpi: Number of micro dots per inch • The ratio dpi/lpi decides the size of the
halftone cell
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lpi & dpi
gray tones of n12
lpidpi umber=+
⎟⎟⎟⎟⎟
⎠
⎞
⎜⎜⎜⎜⎜
⎝
⎛
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lpi & dpi (Example)
• Assume that dpi is fixed at 600 • lpi = 150 only gives 17 gray tones • lpi = 100 only gives 37 gray tones • lpi = 50 gives 145 gray tones • Does a higher lpi always lead to a better
print? Not necessarily!
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High lpi, few gray tones
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Lower lpi, more gray tones
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Low lpi, more gray tones but large halftone dots, (not satisfying)
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AM & FM HALFTONING
• AM (Amplitude Modulated) – The size of the dots is variable, their frequency
is constant • FM (Frequency Modulated) 1st generation
– The size of the dots is constant, their frequency varies
• FM (Frequency Modulated) 2nd generation – The size of the dots and their frequency vary
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AM & FM (1st & 2nd Generation) Halftone
AM FM, 1st FM, 2nd
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AM & FM Halftone
AM FM
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FM Halftone, 1st and 2nd generation
First Second
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Hybrid Halftoning
AM FM_1 FM_2
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THRESHOLDING
⎩⎨⎧
<
≥=
),(),( if ,0),(),( if ,1
),(nmtnmgnmtnmg
nmb
g and b are the original and the halftoned image, respectively.
t is the threshold matrix.
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THRESHOLDING
This threshold matrix represents 10 gray tones
0.6
0.1
1
0.3
0.2 0
Originalbild Rastrerad bildTröskelmatrisOriginal image Halftoned image Threshold matrix
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THRESHOLD MATRIX Example: Line
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
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THRESHOLD MATRIX Example: Spiral
1 2 3 4 12 13 14 5 11 16 15 6 10 9 8 7
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THRESHOLD MATRIX Clustered & Dispersed, 45 degrees
14 12 13 16 19 21 20 17 5 4 3 10 28 29 30 23 6 1 2 11 27 32 31 22 9 7 8 15 24 26 25 18 19 21 20 17 14 12 13 16 28 29 30 23 5 4 3 10 27 32 31 22 6 1 2 11 24 26 25 18 9 7 8 15
1 30 8 28 2 29 7 27 17 9 24 16 18 10 23 15 5 25 3 32 6 26 4 31 21 13 19 11 22 14 20 12 2 29 7 27 1 30 8 28 18 10 23 15 17 9 24 16 6 26 4 31 5 25 3 32 22 14 20 12 21 13 19 11
Clustered Dispersed
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TABLE HALFTONING
Original image Halftoned image
Mean
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TABLE HALFTONING
Clustered Dispersed
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FM HALFTONING Error Diffusion
Original image Halftoned image Error filter
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Error Diffusion
The threshold value is 0.5
Suffers from artifacts, See specially the highlights and shadows and also the mid-tone regions
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Error Diffusion
The threshold value is a random number between 0.25 and 0.75
Better?
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Iterative Method Controlling Dot Placement (IMCDP)
• The original continuous-tone image is scaled between 0 and 1
• 0 and 1 represent white and black respectively • The binary/halftoned image is totally white to
begin with
Assumptions:
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IMCDP
The mean of the density values of the original image corresponds to the area of the inked regions
Original Image Binary Image
The first dot is placed where the original image has its largest density value
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Original Image
IMCDP The impact of the placed dot is fed back to the original image by a filter
The next dot is placed where the modified image has its largest density value
Binary Image
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Iterative Halftoning, IMCDP IMCDP Original
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IMCDP(filter)
• A Gaussian filter is used • Experiments show that an 11 x 11 Gaussian filter
leads to satisfactory results in most cases • The size of the filter should be changing for the
light and dark parts of the original image
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IMCDP(filter)
For halftoning of a constant image with a coverage of p% the size of the filter is decided by:
pa /100=
The size of the filter is (2a + 1) x (2a + 1) rounded
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IMCDP(filter)
11 x 11 filter 21 x 21 filter
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IMCDP
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Models of Visual Perception
{ }1.1)114.0(exp)114.00192.0(6.2)( fffH −+=
f is the frequency in cycles/degree The spacing between the dots is given by:
πτ
1801)21arctan(21
RdRdf=== degrees
R is the printer resolution and d is the viewing distance.
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Models of Visual Perception
Viewing distance, d = 30 inches
Printer resolution, R = 300 dpi
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A simple Printer Model (Dot overlap Model)
α
β
γ T
0 0 0 0 0 0 0
0 0 1 1 0 1 0
0 1 0 0 0 0 0
0 0 0 0 0 0 0
0 β α α 2β α β
β 2α−γ 1 1 2α 1 α
α 1 2α−γ α 2β α β
β α β 0 0 0 0
p(i,j)bb(i,j)
⎩⎨⎧
=
=
−+=
0),(1),(1
),(321 jib
jibifif
fffjip
γβα
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Least Square Model Based Algorithm
EYE MODEL
PRINTER MODEL EYE MODEL
g(Original)
b(Binary)
z
w
2,, )(∑∑ −=
i jjiji wzε The squared error
One way: Start with an initial binary image b. For each pixel (i,j) find the binary value b(i,j) that minimizes ε.
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Objective Quality Measures
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Objective Quality Measure (Halftone Images)
• A method that works well for certain kinds of images, might produce results of low quality for other images
• The definition of a “good” halftoning method may vary from application to application
• There might be a number of requests that cannot be formulated by a simple objective measure
• And so on …
Why difficult?
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Objective Quality Measure (Halftone Images)
• The original grayscale image and the binary image should be as similar as possible (How to define this similarity?)
• The black dots in the highlights (and the “white” dots in the shadows) should be placed homogeneously.
• In color case, the color should also be reproduced as accurate as possible
• And so on …
A number of criteria
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A simple measure
2
,
)),(),((∑ −=ji
jigjibe
g is the original image and b is the resulting binary image
Which image b gives the lowest error e?
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SNR (Signal-to-Noise ratio)
))),(),((
),((log10)(
,
2,
2
10 ∑
∑−
=
ji
ji
jibjig
jigdBSNR
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SNR
• These kinds of measures are very easy to apply but they assume that the distortion is only caused by additive noise.
• These measures don’t correlate well with our perceived visual quality
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Quantization Noise Spectrum (QNS)
),(),(),( jibjigjiq −=
2),( lkQ
The quantization noise is defined as:
The quantization noise spectrum (QNS) is defined as:
Q is the 2-dimensional Fourier transform of q
The smaller the quantization noise spectrum, the more similar b and g are.
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Similarity
By similarity we mean the perceptual similarity. Since the eye acts as a low-pass filter it is desirable that the QNS is is small in the low pass region, that means:
∑Ω
=2),( lkQe
is small
Ω denotes a low-pass region.
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QNS (Example)
Error diffusion IMCDP
g = 1/32
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QNS • The error e has been calculated for the images shown in
previous slide when W is a circular low-pass region that occupy 12.5% of the image. The error is slightly smaller for the image halftoned by ED than the one by IMCDP!!!!
• Therefore: It is not only the magnitude of the QNS in the low-pass region that is important. The shape of QNS also plays a significant role.
• Desirable: A more or less circularly symmetric QNS with small magnitude in the low pass region
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QNS (Example)
Error diffusion IMCDP
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QNS (Example)
Error diffusion IMCDP
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Homogeneousness • One way of studying the characteristic of a halftoning
method is to study the halftone patterns (tints) produced by the method. By a halftone pattern we mean the result of halftoning a constant image.
• We want the dots in the halftone pattern to be placed as homogeneously as possible over the entire image – The set of distances from each dot to its closest dot gives a good
picture of how close/far the dots in the halftone pattern are placed. The couple mean value and standard deviation of the data in this set can be used as a measure for homogeneousness of the pattern. (NOTE: Useful for very light and dark tones only)
• Desirable: Big mean value and small standard deviation
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Homogeneousness
11 x 11 filter 21 x 21 filter
(Mean value, standard deviation)=(7.28, 1.19) for the image to the left and (8.76, 0.82) for the image to the right
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Frequency Response
Original
ED (Floyd & Steinberg filter)
ED (Jarvis-Judice-Ninke filter)
IMCDP
The frequency is increased from left to right
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Frequency Gain
Use the original image in the previous page as the input image and Compute the frequency gain:
in
out
II
fG =)(
Iout and Iin are the Fourier transform of the output and the input Image, respectively.
Desirable: G(f) is close to 1 at low frequencies.
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Frequency Gain
ED (F & S) ED (J & J & N)
IMCDP
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Frequency Gain
• From the previous diagrams we see that error diffusion methods have a tendency of high-pass filtering (edge enhancement) the original image
• The frequency gain for the image halftoned by IMCDP is very close to 1 at low frequencies
• The gain at higher frequencies are not of any particular interest because the eye is less sensitive there
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Halftone Image Quality
• A method that works well for certain images, might produce results of low quality for other images. An image with two gray levels (0.49 in the left half and 0.5 in the right half) is halftoned by Floyd-Steinberg error diffusion
• While the border between these two gray levels are hardly detected by the eye, it is emphasized by error diffusion because of a sudden change of pattern structure
Original image Error diffusion
0.49 0.5