idl gui for digital halftoning final project for simg-726 computing for imaging science changmeng...
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IDL GUI for Digital Halftoning
Final Project for SIMG-726 Computing For Imaging Science
Changmeng Liu2.14.2004
Outline Objective Digital Halftoning Background Halftoning algorithm Resolution of the display device
and Human Vision MTF and CSF GUI design and demo Conclusion and Future Work
Objective A IDL GUI is implemented to display
the halftone image created by different Digital Halftoning algorithms.
The GUI can simulate the Halftone image in different resolution
Dispersed-dot, Clustered-dot with different orientation, and Random dithering algorithms are implemented
What is Halftone and Why Halftone
A process converts a gray scale image into a bi-level image or more generally to fewer gray levels.
Applications: a. Printing images b. Display images with low-end
display unit c. compressing images & video clips
Continuous Tone image vs. Halftone image
Continuous tone image: every pixel has its continuous valued gray level, for example from 0 to 255
Halftone image: every pixel only has two gray levels: 0 and 1
Example of continuous tone and halftone image
Halftone cell --- Resolution vs. Represent gray level There is a trade off in halftone
technique, the resolution vs. represent gray level
Larger halftone cell can represent more gray level, but larger halftone cell will reduce the resolution of the halftone image.
Note: Error diffusion has no such limitation
Halftone Algorithm The Ordered Dithering Algorithm flow
chart
image
screen
halftone
compare pixel-by-pixel
1 if image > screen0 otherwise
8 bpp
ready for printing on a binary printer8 bpp
1 bpp
Cluster-dot Dithering In its simplest form, a digital version of
analog methods Used in higher resolution printing
devices where system response is nonlinear
Clustering the black pixels yields repeatable, low noise images with acceptable tone reproduction characteristics
Screen Cell The screen is usually constructed
from small rectangular bricks, called cells
98
28
84
14
70 126
56
140
210
196
252
182
238
16822415411242
This screen is constructed from 3x6 cells; the cells are offset by 3 pixels.
Single Cell Construction. The screen on the left with this cell form a 45 degree screen
Halftone Cell Orientation Human Vision have lowest contrast
sensitivity at 45° For monochrome screens (e.g., B&W),
cells are typically nx2n in dimension and offset so the lowest frequency component is at 45°, where the visual MTF is lowest.
m
m
0°
2n
n
45°
Dispersed-Dot Dithering Dispersed-dot produces finer
image details, and its halftone texture pattern contains much higher frequency and therefore is less visible
Bayer’s mask is used as the dispersed-dot dithering mask in this project
Dispersed-Dot Mask Design Recursively Define mask M(k), for
K>0
M(k) has dimensions 2k * 2k
Random Dithering White noise dithering use an
uniformly distributed uncorrelated noise as threshold function.
Random dithering has visible low frequency noise
Resolution of the display device DPI refers to the number of dots (pixels)
per inch on a screen. Macintosh computer (or MacOS
compatible), have a 72 dpi screen. Windows PC have a 96 dpi screen.
Because of this, objects that are displayed under Windows will appear to be 133% of their printed size (at default 72 dpi).
Printer have 72 dpi (default), 300dpi, 600dpi or even higher resolution.
Question Can I Simulate the High Resolution
Halftone Image (300 dpi) with a Low Resolution CRT(96 dpi)?
- No, you can’t display it by just down sampling the high resolution halftone image directly because of the Aliasing.
- But, you can simulate the appearance of the high resolution ht image in CRT.
Human Visual MTF Human
Visual MTF is a kind of low pass filter
Human Visual CSF Contrast Sensitivity
Function of Human Visual system is a band pass filter
From the MTF and CSF, the high spatial frequency in high resolution printer can’t be “seen” by human eye
Simulate different resolution halftone image on CRT Up-sample (or scale) the image to
high resolution size. Applying halftone algorithm Convolve the halftone image with
the low pass filter to simulate what our eye sees
Down-sample the low-passed image to real size
GUI design, flow chart
Dithering
Image Input
Halftone Algorithm Select
Display Resolution Select
Halftone Image Display
Save the Halftone Image
Input Image Display
Output 1, 0° Cluster-dot at 96 dpi
Output 2, 0° Cluster-dot at 300 dpi
Output 3, 45° Cluster-dot at 96 dpi
Output 4, 45° Cluster-dot at 300 dpi
Output 5, Dispersed-dot at 96 dpi
Output 6, Dispersed-dot at 300 dpi
Output 7, Random dithering at 96 dpi
Output 8, Random dithering at 300 dpi
Conclusion and Future Work This GUI can simulate the different
resolution halftone image display Cluster-dot (45° and 0°), disperse-dot, and
random dot is implemented. The convolution kernel can be changed
to fit the human visual MTF and CSF better.
More halftone algorithms can be implemented.
Question?
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