Presentation of our APGV 09 paper: Quality Assessment of Fractalized NPR Textures: a Perceptual Objective Metric (http://artis.inrialpes.fr/Publications/2009/BTS09/)

1. Quality Assessment of Fractalized NPR Textures:
a Perceptual Objective Metric
Pierre Bénard Joëlle Thollot François Sillion
Grenoble Universities and CNRS / LJK
INRIA
October 2, 2009
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2. Introduction
Introduction
[GTDS04]
• Non-Photorealistic Rendering
 Inspiration in traditional illustration (drawing, painting...)
 Stylization of still images and animations
• Stylization of 3D scenes
 3D scenes  2D media (pigments, strokes, paper...) [Herz98]
 Temporal coherence artifacts
(popping, sliding, deformations)
« Il pleut bergère », Jérémy Depuydt (2005)
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3. Introduction
Introduction
• Common solution: Fractalization process
(e.g. [KLK+00, CTP+03, BSM+07, BBT09])
 Medium = texture
 Computation of multiple scales
 Alpha-blending
 Self-similar texture
+
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4. Introduction
Introduction
• Common solution: Fractalization process
(e.g. [KLK+00, CTP+03, BSM+07, BBT09])
 Self-similar texture
+
[BBT09]
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5. Introduction
Problem statement
• Issues:
New features / new frequencies
≠

 Global contrast loss
 Deformations
Fractalized textures visually dissimilar to the original
 How to evaluate this dissimilarity ?
• Artists / viewers = final judges of the perceived quality
User study suitable
… but costly
• Quality assessment metric
 automatic comparison of existing techniques
 new optimization based approaches
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6. Introduction
Problem statement
original
• Input: pairs of 2D texture 
transformed
• Definition:
texture distortion = visual dissimilarity between
original and transformed textures
• Goal: define a quantitative metric of this distortion
• Procedure:
 User study  ranking of texture pairs
according to their distortion
 Statistical analysis  scale of perceived quality
 Correlation investigation  objective metric
• Restrictions: 1.2
-1.0 -0.5 0.0 0.5 1.0
Z-Scores
 No texture mapping 1.0
ACE
0.8
 Static images 0.6
0.4
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7. Outline
 Previous Work
 Experimental Framework
 Statistical Analysis
 Correlation with Objective Metrics
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8. Previous Work
Outline
 Previous Work
 Perceptual Evaluation in NPR
 Perceptual Experiment Methodologies
 Experimental Framework
 Statistical Analysis
 Correlation with Objective Metrics
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9. Previous Work
Perceptual Evaluation in NPR
• Various methodologies: [SSLR96]
 Questionnaire [SSLR96]
 Performances measurement [GRG04]
 Eye tracking [SD04]
 Observational study + objective metric [INC+06, MIA+08]
[SD04]
[GRG04]
[INC+06 ,MIA+08]
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10. Previous Work
Perceptual Experiment Methodologies
• Rating – ex.: image and video quality assessment [SSB06, Win05]
 Simple, well-understood
 Large number of trials and participants, subjects training needed
• Paired comparisons – ex.: tone mapping comparison [LCTS05, ČWNA08]
 Straightforward forced choices
 Quadratic complexity  effect of fatigue
• Ranking – ex.: high quality global illumination [SFWG04]
 Least time consuming task, invariant under stretching
 Complicated task
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11. Experimental Framework
Outline
 Previous Work
 Experimental Framework
 Stimuli
 Procedure
 Statistical Analysis
 Correlation with Objective Metrics
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12. Experimental Framework
Stimuli
• 2 sets of 10 textures pairs of NPR media
(Near-)regular Irregular Cross-
Grid Dots Hatching Paper Paint Pigments Noise
patterns patterns hatching
S1
S2
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13. Experimental Framework
Stimuli
• 2 sets of 10 textures pairs of NPR media
• Fractalized with 3 scales
(Near-)regular Irregular Cross-
Grid Dots Hatching Paper Paint Pigments Noise
patterns patterns hatching
Original
S1
Transformed
Original
S2
Transformed
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14. Experimental Framework
Procedure
• Dynamic web interface Naive 58.0%
 Number of participants (103) Amateur/professional
8.5%
 Diversity of their skills in computer graphics infographists
Researcher 22.4%
 Control on the experimental conditions
Unknown 11.1%
 Assessment of the statistical validity
of the resulting data
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15. Experimental Framework
Procedure
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16. Statistical Analysis
Outline
 Previous Work
 Experimental Framework
 Statistical Analysis
 Ranking Duration
 Concordance among Raters
 Interval Scale of Relative Perceived Distortion
 Ranking Criteria
 Correlation with Objective Metrics
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17. Statistical Analysis
Ranking Duration
• 103 subjects
 45 starting with S1 then S2
1 2
1 2
 58 starting with S2 then S1
• Similar distribution of duration
• Comparable mean duration +
 No learning or fatigue effect
+
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18. Statistical Analysis
Concordance among Raters
• Merged data analysis relevant
 Kendall’s coefficient of concordance (Kendall’s W) [Ken75]
• Ranking not effectively random
 Significance of these coefficients validated by test
• Strong variations among textures pair
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19. Statistical Analysis
Interval Scale of Relative Perceived Distortion
• Ordinal scale  no quantification of perceived differences between pairs
S1
S2
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20. Statistical Analysis
Interval Scale of Relative Perceived Distortion
• Ordinal scale  no quantification of perceived differences between pairs
Thurstone’s law of comparative judgment [Tor58]
Regular Irregular Cross-
Grid Hatching Dots Pigments Paint Paper Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
S1
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
S2
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21. Statistical Analysis
Interval Scale of Relative Perceived Distortion
• Unstructured textures more robust
• Textures with distinctive features more severely distorted
Regular Irregular Cross-
Grid Hatching Dots Pigments Paint Paper Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
S1
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
S2
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22. Statistical Analysis
Interval Scale of Relative Perceived Distortion
• Perceived similar distortion intensity significance
 Wilcoxon rank sum test
Regular Irregular Cross-
Grid Hatching Dots Pigments Paint Paper Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
S1
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
S2
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23. Statistical Analysis
Interval Scale of Relative Perceived Distortion
 Overall contrast of patterns
 Feature shapes
Regular Irregular Cross-
Grid Hatching Dots Pigments Paint Paper Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
S1
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
S2
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24. Statistical Analysis
Interval Scale of Relative Perceived Distortion
 Overall contrast of patterns
 Feature shapes
Regular Irregular Cross-
Grid Hatching Dots Pigments Paint Paper Noise
Pigments
patterns Paint patterns hatching Paper Noise Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
S1
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
S2
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25. Statistical Analysis
Interval Scale of Relative Perceived Distortion
 Overall contrast of patterns
 Feature shapes
Regular Irregular Cross-
Grid Hatching Dots Pigments Paint Paper Noise
Hatching
patterns Dots patterns Irregular patterns
hatching Cross-hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
S1
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
S2
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26. Statistical Analysis
Interval Scale of Relative Perceived Distortion
 Overall contrast of patterns
 Feature shapes
Regular Irregular Cross-
Grid Hatching Dots Pigments Paint Paper Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
S1
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
S2
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27. Statistical Analysis
Ranking Criteria
 Quite similar frequencies
40%
Frequency at which each criterion has been used
35%
30%
contrast
25%
sharpness
20% scale
other
15%
empty
10%
5%
0%
S1 S2
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28. Statistical Analysis
Ranking Criteria
contrast
 Quite similar frequencies sharpness
scale
• Irregular preferences for different textures others
empty
60%
S1 S2
50%
40%
30%
20%
10%
0%
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29. Statistical Analysis
Ranking Criteria
 Quite similar frequencies
• Irregular preferences for different textures
• Top 3 additional criteria proposed by the participants:
 Pattern coherence
 Density
 Shape
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30. Correlation with Objective Metrics
Outline
 Previous Work
 Experimental Framework
 Statistical Analysis
 Correlation with Objective Metrics
 Image Quality Metric / Global Image Statistic
 Local Image Statistic
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31. Correlation with Objective Metrics
Image Quality Metrics / Global Image Statistic
peak signal-to-noise ratio (PSNR)
• 11 quality assessment metrics
signal-to noise ratio (SNR)
 MetrixMux Matlab© package by Matthew Gaubatz
(http://foulard.ece.cornell.edu/gaubatz/metrix_mux/) structural similarity index (SSIM)
multi-scale SSIM index (MSSIM)
 No significant correlation visual signal-to-noise ratio (VSNR)
 distortion too strong visual information fidelity (VIF)
pixel-based VIF (VIFP)
information fidelity criterion (IFC)
Histograms universal quality index (UIQ)
• 3 global statistics Power spectra noise quality measure (NQM)
Distribution of contrast [BG93] weighted signal-to-noise ratio (WSNR)
 Inconclusive results
 consider simultaneously contrast, sharpness and scale
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32. Correlation with Objective Metrics
Local Image Statistic
• Gray level co-occurrence matrix (GLCM) [HSD73]
 Texture descriptor [TJ93]
 Local image property
 Match certain levels of human perception [JGSF76]
 Linked to density and pattern coherence criteria
 Parameters selection
• Average Co-occurrence Error [CRT01]
 High correlation with the perceptual interval scale
(Person’s correlation: 0.953 for S1 and 0.836 for S2 )
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33. Correlation with Objective Metrics
Interval Scale of Relative Perceived Distortion
Regular Irregular Cross-
Grid Hatching Dots patterns hatching Pigments Paint Paper Noise
patterns
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
1.2
1.0
0.8
S1
0.6
0.4
ACE
r² = 0.9075
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
1.4
1.2
1.0
S2
0.8
0.6
ACE
r² = 0.6992
ACE relevant estimator of the distortion
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34. Conclusions
Conclusions
• First step toward the evaluation of texture-based NPR techniques
 10 classes of NPR medium
 User-study framework
 Dataset and analysis methodology
 Quality assessment metric: ACE
• Future work:
 Other texture or vision descriptors
 Dynamic version of the fractalization process
 Trade-off between temporal continuity and texture dissimilarity
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35. Conclusions
Thank you for your attention
• Project web page
http://artis.inrialpes.fr/~Pierre.Benard/TextureQualityMetric/
 Dynamic web interface
 Full-size figures
 R scripts
• Acknowledgments
 All the participants of the study
 Jean-Dominique Gascuel, Olivier Martin, Fabrice Neyret, Pierre-Édouard
Landes, Pascal Barla, Alexandrina Orzan and the anonymous reviewers
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36. Thurstone’s law of comparative judgement
• Proportion of each pair:
Normal distribution
• Conversion to z-Scores: assumption
with the mean and the standard deviation of these proportions
S1 S2
• Empirically verified
 Normal Q-Q plots
 Shapiro-Wilk test
 Better confidence
for S2 than S1
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37. Wilcoxon rank sum test (Mann-Witney U test)
• Wilcoxon rank sum test
 Non-parametric test
 Assess if two independent samples of observations come from
the same distribution
 No assumption about this distribution
 Null hypothesis H0:
“the two considered samples are drawn from a single population”
 Group pairs for which H0 cannot be rejected
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38. Statistical Analysis
Interval Scale of Relative Perceived Distortion
 Overall contrast of patterns
 Feature shapes
Regular Irregular Cross-
Grid Hatching Dots Pigments Paint Paper Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0
S1
Irregular Near-regular Cross-
Dots Hatching Grid Paper Paint Pigments Noise
patterns patterns hatching
Z-Scores
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
S2
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39. Statistical Analysis
Ranking Criteria
contrast
 Quite similar frequencies sharpness
scale
• Irregular preferences for different textures others
empty
60%
S1 S2
50%
40%
30%
20%
10%
0%
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40. Statistical Analysis
Ranking Criteria
• Proposed criteria:
contrast sharpness scale other empty
S1 21.96% 26.86% 24.83% 14.4% 8.95%
S2 28.27% 35.21% 21.72% 7.12% 7.07%
• Additional criteria proposed by participants:
density 15% 
shape 10%
pattern coherence 21% 
frequency 4%
relief 2%
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41. Correlation with Objective Metrics
Local Image Statistic
• Gray Level Co-occurrence Matrices
 n matrices of size G x G
1
 n = number of displacement vectors
1
 G = gray-level quantization step 1
G
 = number of occurrence of gray-level pair
a distance d apart G
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42. Correlation with Objective Metrics
Local Image Statistic
• Gray Level Co-occurrence Matrices
 n matrices of size G x G
1
 n = number of displacement vectors
1
 G = gray-level quantization step 1
G
 = number of occurrence of gray-level pair 1
a distance d apart G
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43. Correlation with Objective Metrics
Local Image Statistic
• Average Co-occurrence Error [CRT01]
 Distance between 2 sets of GLCM
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44. References I
[BBT09] Pierre Bénard, Adrien Bousseau, and Joëlle Thollot, Dynamic solid textures for real-time coherent stylization, ACM
SIGGRAPH Symposium on Interactive 3D Graphics and Game, 2009.
[BG93] Rosario M Balboa and Norberto M Grzywacz, Power spectra and distribution of contrasts of natural images from different
habitats, Vision Research (1993).
[BSM+07] Simon Breslav, Karol Szerszen, Lee Markosian, Pascal Barla, and Joëlle Thollot, Dynamic 2D patterns for shading 3D
scenes, SIGGRAPH 07: ACM Transactions on Graphics (2007).
[CRT01] A.C. Copeland, G. Ravichandran, and M.M. Trivedi, Texture synthesis using gray-level co-occurrence models, algorithms,
experimental analysis and psychophysical support, Optical Engineering (2001).
[CTP+03] Matthieu Cunzi, Joëlle Thollot, Sylvain Paris, Gilles Debunne, Jean-Dominique Gascuel, and Frédo Durand, Dynamic
canvas for immersive non-photorealistic walkthroughs, Proceedings of Graphics Interface, 2003.
[CWNA08] Martin Cadík, Michael Wimmer, Laszlo Neumann, and Alessandro Artusi, Evaluation of HDR tone mapping methods using
essential perceptual attributes, Computers & Graphics (2008).
[GRG04] Bruce Gooch, Erik Reinhard, and Amy Gooch, Human facial illustrations: Creation and psychophysical evaluation, ACM
Trans. Graph. 23 (2004), no. 1, 27–44.
[HSD73] Robert M. Haralick, K. Shanmugam, and Its’Hak Dinstein, Textural features for image classification, Systems, Man and
Cybernetics, IEEE Transactions on 3 (1973), no. 6, 610–621.
[INC+06] Tobias Isenberg, Petra Neumann, Sheelagh Carpendale, Mario Costa Sousa, and Joaquim A. Jorge, Non-photorealistic
rendering in context: an observational study, NPAR ’06: Symposium on Non-photorealistic animation and rendering, ACM,
2006.
[JGSF76] B. Julesz, E. N. Gilbert, L. A. Shepp, and H. L. Frisch, Inability of humans to discriminate between visual textures that
agree in second-order statistics –revisited, Perception (1976).
[Ken75] M. G. Kendall, Rank correlation methods, Hafner Publishing Company, Inc, 1975.
Bénard – Thollot – Sillion (LJK - INRIA) Quality Assessment of Fractilized Textures October 2, 2009 44 /32

45. References II
[KLK+00] Allison W. Klein, Wilmot W. Li, Michael M. Kazhdan, Wagner T. Correa, Adam Finkelstein, and Thomas A. Funkhouser,
Non-photorealistic virtual environments, Proceedings of SIGGRAPH 2001, ACM, 2000.
[LCTS05] Patrick Ledda, Alan Chalmers, Tom Troscianko, and Helge Seetzen, Evaluation of tone mapping operators using a high
dynamic range display, SIGGRAPH 05 : ACM Transactions on Graphics (2005).
[MIA+08] Ross Maciejewski, Tobias Isenberg, William M. Andrews, David S. Ebert, Mario Costa Sousa, and Wei Chen, Measuring
stipple aesthetics in hand-drawn and computer-generated images, IEEE Comput. Graph. Appl. (2008).
[SD04] Anthony Santella and Doug DeCarlo, Visual interest and NPR: an evaluation and manifesto, NPAR ’04: Symposium on
Non-photorealistic animation and rendering, ACM, 2004.
[SFWG04] William A. Stokes, James A. Ferwerda, Bruce Walter, and Donald P. Greenberg, Perceptual illumination components: a
new approach to efficient, high quality global illumination rendering, ACM Transactions on Graphics (2004).
[SSB06] H.R. Sheikh, M.F. Sabir, and A.C. Bovik, A statistical evaluation of recent full reference image quality assessment
algorithms, IEEE Transactions on Image Processing (2006).
[SSLR96] Jutta Schumann, Thomas Strothotte, Stefan Laser, and Andreas Raab, Assessing the effect of non-photorealistic rendered
images in cad, CHI ’96: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, 1996.
[TJ93] Mihran Tuceryan and Anil K. Jain, Texture analysis, The Handbook of pattern recognition & computer vision (1993).
[Tor58] W. S. Torgerson, Theory and methods of scaling, Wiley, 1958.
[Win05] Stefan Winkler, Perceptual video quality metrics – a review, Digital Video Image Quality and Perceptual Coding, CRC
Press, 2005.
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