Histograms Analysis of the Microstructure of Halftone Images

1
Histograms Analysis of the Microstructure of
Halftone Images

• J.S. Arney Y.M. WongCenter for Imaging
  Science, RIT
• Given byLinh V. TranITN, Campus Norrköping,
  Linköping University
• In Digital Halftoning Course. Jan. 17, 2003

2
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 Toolbox
• Developed in University of Texas at Austin, TX,
  USA
• Comparison several halftoning methods
• Done by Michael Bruce deLeon, Stanford, USA

3
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.

4
Ideal case

• Perfect ink drops
• No dot gain

5
Microdensitometry
CCDCamera

• CCD Camera1000x1000 pixels
• Can measure also
• Resolutions
• Granularity
• Micro-distribution of color in the image

Microscope
paper
6
Experiments

• Histogram of 65 LPI AM halftone printed by offset
  lithography, measured at 5 mm field of view (FOV)

7
More Difficult

• Histograms at 5mm FOV of error diffusion dot
  pattern printed by thermal ink jet at 300 dpi
  with F 0.5

8
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

9
Modelling the Bimodal Histogram
10
Frequency Occurence of R
11
Add Gaussian Noise
12
Curve Fitting
? Five unknowns Rmax Rmin a, b
13
Inverse Model
14
Implementation

• Main results published earlier in Wongs 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

15
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)

16
Figures of Merit

• PSNR Peak Signal to Noise Ratio of the output
  image with respect to the input image in dB

17
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

18
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)

19
Demo

• http//www.ece.utexas.edu/bevans/projects/halfton
  ing/toolbox/

20
DeLeons Comparison

• Done by Michael Bruce deLeon, Stanford,
  USAhttp//ise0.stanford.edu/mdeleon/
• Methods
• Bayer Dither Matrix 8x8 matrix
• Three Level Dither
• Error Diffusion Floyd and Steinberg
• MBVQ Error Diffusion(Minimum Brightness
  Variation Quadrants)
• Test images Ramps, Trees, Lena, Chart

21

• Original Image
• Bayer Dither Matrix
• 3 Level Dither
• Error Diffusion
• MBVQ Error Diffusion

22

• Original Image
• Bayer Dither Matrix
• 3 Level Dither
• Error Diffusion
• MBVQ Error Diffusion

23
Tree image
24
(No Transcript)
25
Tree Image
26
(No Transcript)
27
Lena Image
28
(No Transcript)
29
Lena Image
30
(No Transcript)
31
Chart Image
32
(No Transcript)
33
Chart Image
34
(No Transcript)
35
DeLeons Conclusions

• Solid tones seem the most difficult to present
  smoothly witha 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.

36
DeLeons 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.

37
(No Transcript)