Histograms Analysis of the Microstructure of Halftone Images J.S. Arney & Y.M. Wong Center for...
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Transcript of Histograms Analysis of the Microstructure of Halftone Images J.S. Arney & Y.M. Wong Center for...
Histograms Analysis of the Microstructure of Halftone Images
Histograms Analysis of the Microstructure of Halftone Images
J.S. Arney & Y.M. WongCenter for Imaging Science, RIT
Given byLinh V. Tran
ITN, Campus Norrköping, Linköping University
In Digital Halftoning Course. Jan. 17, 2003
Linh V. Tran - Graduate course in Digital Halftoning 2/36
Outline• J.S. Arney & Y.M. Wong. ”Histograms Analysis
of the Microstructure of Halftone Images”. 1999– Problem definition
• Ideal case• More Complicated cases in Reality
– Solution: Modeling the bimodal histogram– Experiments
• MatLab Halftoning ToolboxDeveloped in University of Texas at Austin, TX, USA
• Comparison several halftoning methodsDone by Michael Bruce deLeon, Stanford, USA
Linh V. Tran - Graduate course in Digital Halftoning 3/36
Problem
• Estimate– The mean reflectance of the paper between the
halftone dots, RP
– The mean reflectance of the dots, RI and
– The halftone dot area fraction, F
of a given printed patch.
Linh V. Tran - Graduate course in Digital Halftoning 4/36
Paper
Ink
• Perfect ink drops• No dot gain
Ideal case
F
1-F
0 Ri Rp 1
A perfect frequency occurrence of gray levels of reflectance consists of 2 delta functions.
Linh V. Tran - Graduate course in Digital Halftoning 5/36
Microdensitometry
CCDCamera
CCDCamera
MicroscopeMicroscope
paper
• CCD Camera:1000x1000 pixels
• Can measure also- Resolutions- Granularity- Micro-distribution of
color in the image
Linh V. Tran - Graduate course in Digital Halftoning 6/36
Experiments
• Histogram of 65 LPI AM halftone printed by offset lithography, measured at 5 mm field of view (FOV)
Linh V. Tran - Graduate course in Digital Halftoning 7/36
More Difficult
• Histograms at 5mm FOV of error diffusion dot pattern printed by thermal ink jet at 300 dpi with F = 0.5
Linh V. Tran - Graduate course in Digital Halftoning 8/36
More and More Difficult
• Histograms at 5mm FOV of error diffusion dot pattern printed by thermal ink jet at 300 dpi with F = 0.05
Linh V. Tran - Graduate course in Digital Halftoning 9/36
Modelling the Bimodal Histogram
minminmax R
bxaexp
RRR
)(1
The edge modeled withRmin = 0.3, Rmax = 0. 7a = 10, and b = 0.5
Linh V. Tran - Graduate course in Digital Halftoning 10/36
Frequency Occurence of R
1
)(
)(1
dx
dRRH
Rbxaexp
RRR min
minmax
dx
Linh V. Tran - Graduate course in Digital Halftoning 11/36
Add Gaussian Noise
2
2
2
)5.0(
2
1)(
R
expRS
Linh V. Tran - Graduate course in Digital Halftoning 12/36
Curve FittingFive unknowns: Rmax
Rmin
a, b
Linh V. Tran - Graduate course in Digital Halftoning 14/36
Implementation
• Main results published earlier in Wong’s B.Sc. Thesis:
”Modeling the Halftone Image to Determine the Area Fraction of Ink”
CIS, RIT, 1998
• www.cis.rit.edu/research/thesis/bs/1998/wong• Simulations mainly done in MathCAD
Linh V. Tran - Graduate course in Digital Halftoning 15/36
Halftoning MatLab Toolbox Developed in University of Texas at Austin, TX, USA
• Grayscale halftoning methods– Classical and user-defined screens– Classical error diffusion methods– Edge enhancement error diffusion– Green noise error diffusion– Block error diffusion
• Figures of merit measures for grayscale halftones– Peak signal-to-noise ratio (PSNR)– Weighted signal-to-noise ratio (WSNR)– Linear distortion measure (LDM)– Universal quality index (UQI)
Linh V. Tran - Graduate course in Digital Halftoning 16/36
Figures of Merit
• PSNR: Peak Signal to Noise Ratio of the output image with respect to the input image in dB
2
2
1010InOut
velpeakgrayleimsizelogPSNR
Linh V. Tran - Graduate course in Digital Halftoning 17/36
Figures of Merit
• WSNR: Weighted Signal to Noise Ratio of output image with respect to the input image in dB. A weighting appropriate to the human visual system is used.J. Mannos and D. Sakrison, "The effects of a visual fidelity criterion on the encoding of images", IEEE Trans. Inf. Theory, IT-20(4), pp. 525-535, July 1974
• LDM: Linear Distortion Ratio.
• UQI: Universal image Quality Index.Zhou Wang and Alan C. Bovik "A Universal Image Quality Index" IEEE Signal Processing Letters, 2001
Linh V. Tran - Graduate course in Digital Halftoning 18/36
Halftoning MatLab Toolbox• Color halftoning methods
– Classical and user-defined (multilevel) screens (separable)– Classical separable error diffusion methods (separable)– Edge enhancement error diffusion (separable)– Green noise error diffusion (separable)– Block error diffusion (separable)– Minimum brightness variation quadruple error diffusion (non-
separable design for separable implementation)– Vector error diffusion (non-separable)
• Figures of merit measures for color– PSNR, WSNR, LDM, UQI as in grayscale halftoning– Noise gain in dB over Floyd-Steinberg error diffusion
(specific to Vector Error Diffusion)
Linh V. Tran - Graduate course in Digital Halftoning 19/36
Demo
• http://www.ece.utexas.edu/~bevans/projects/halftoning/toolbox/
Linh V. Tran - Graduate course in Digital Halftoning 20/36
DeLeon’s Comparison
• Done by Michael Bruce deLeon, Stanford, USAhttp://ise0.stanford.edu/~mdeleon/
• Methods:1. Bayer Dither Matrix: 8x8 matrix 2. Three Level Dither3. Error Diffusion: Floyd and Steinberg4. MBVQ Error Diffusion
(Minimum Brightness Variation Quadrants)
• Test images: Ramps, Trees, Lena, Chart
Linh V. Tran - Graduate course in Digital Halftoning 21/36
• Original Image
• Bayer Dither Matrix
• 3 Level Dither
• Error Diffusion
• MBVQ Error Diffusion
Linh V. Tran - Graduate course in Digital Halftoning 22/36
• Original Image
• Bayer Dither Matrix
• 3 Level Dither
• Error Diffusion
• MBVQ Error Diffusion
Linh V. Tran - Graduate course in Digital Halftoning 23/36
Tree image
Original Image Bayer Dither Matrix
Three Level Dither Error Diffusion
Linh V. Tran - Graduate course in Digital Halftoning 25/36
Tree Image
MBQV Error Diffusion Bayer Dither Matrix
Three Level Dither Error Diffusion
Linh V. Tran - Graduate course in Digital Halftoning 27/36
Lena Image
Original Image Bayer Dither Matrix
Three Level Dither Error Diffusion
Linh V. Tran - Graduate course in Digital Halftoning 29/36
Lena Image
MBQV Error Diffusion Bayer Dither Matrix
Three Level Dither Error Diffusion
Linh V. Tran - Graduate course in Digital Halftoning 31/36
Chart Image
Original Image Bayer Dither Matrix
Three Level Dither Error Diffusion
Linh V. Tran - Graduate course in Digital Halftoning 33/36
Chart Image
MBQV Error Diffusion Bayer Dither Matrix
Three Level Dither Error Diffusion
Linh V. Tran - Graduate course in Digital Halftoning 35/36
DeLeon’s Conclusions• Solid tones seem the most difficult to present smoothly with
a halftoning pattern. Thus, simple computer graphics maybe more of a challenge for a printer than complex photos.
• The color error diffusion algorithm can effectively limit the number of colors used for a given region. Its execution time is only marginally longer than that of regular error diffusion. The pattern produced is slightly smoother than the regular error diffusion results, though unless closely examined in these monitor examples, the differences in dot brightness & color is easy to miss. Depending in its use with actual inks, tradeoffs might have to be made between the appearancesof colors in grayscale images and this smoothing effect.
Linh V. Tran - Graduate course in Digital Halftoning 36/36
DeLeon’s Conclusions
• Multi-level halftoning seems to offer considerable image quality improvement without expensive algorithms. Although the expenses for realizing this functionality come from other areas (cost of extra inks, complexity of multi-drop or variable drop print head), the results would probably justify the extra overhead.
• Model-based halftoning seems like an interesting way to make use of our understanding of the human visual system, but the complexity of these algorithms seems to limit their usefulness for the time being.