1. Structure and Aesthetics in Non-Photorealistic Images
Hua Li, David Mould, and Jim Davies
Carleton University

2. Artistic or Messed
2/35

3. Related Work on Evaluating Non-Photorealistic Algorithms
Performance based on processing speed
ill-suited for stylization
Side-by-side comparisons
not fully convinced by audience
3/35

4. Perceptual Evaluation on Non-Photorealistic Algorithms
Quantitative evaluation
rating scores [Schumann et al. 96, Gooch and Willemsen 02, Mandryk et al. 2011, Mould et al. 2012]
response time [Gooch et al. 04]
eye-tracking data [Mandryk et al. 2011, Mould et al. 2012]
Qualitative evaluation
questionnaire-based
4/35

5. Motivation of Our Study Tone  Structure
Tone-based
Structure-based
Halftoning
[Pang et al. 08] [Chang et al. 09] [Ours 10]
Stippling
[Secord 02]
[Mould 07] [Martin et al. 11] [Ours 11]
Screening
[Ulichney 98]
Abstraction
[Kyprianidis 11]
[Mould 12, 13]
[Floyd and Steinberg 76]
[Ostromoukhov 01]
[Qu et al. 08]
[Ours 11]
5/35

6. Questions to Answer Are structural and aesthetic quality related?
Do images matter for side-by-side comparisons?
6/35

7. Participants 30 participants 15 female and 15 male 11 artists
aged 18 to 33
7/35

8. Study Overview 1 ~ 1.5 hours to complete the experiment
Using the keyboard or the mouse to enter their responses
Tasks:
rating structural and aesthetic quality
collecting response times for rendered images
8/35

9. Image Stimuli Seven categories
include cars, cats, persons, flowers, buildings, mugs, and birds.
Each category contains 13 different images including
one unprocessed image
and 12 rendered images using 12 algorithms.
Images are black and white, or greyscale to remove the influence of color.
9/36

10. Procedure Step 1: verbal introduction Step 2: training
Step 3: formal study
Step 4: questionnaire
Step 5: ranking
10/35

11. Interfaces Used
11/35
Interface for collecting the response time

12. Interfaces Used
Aesthetic rating
Structural rating
12/35

13. Experimental Images -Bird Category
Unprocessed
13/35

14. Experimental Images - Bird Category
Structure-Aware
Structure-Preserving Stippling (SPS)
14/35

15. Experimental Images - Bird Category
Structure-Aware
Content-Sensitive Screening (CSS)
15/35

16. Experimental Images - Bird Category
Structure-Aware
SPS with Exclusion Masks (SPH)
16/35

17. Experimental Images - Bird Category
Structure-Aware
Line Art using edge tangent field (ETF)
17/35

18. Experimental Images - Bird Category
Structure-Aware
Artistic Tessellation (AT)
18/35

19. Experimental Images - Bird Category
Structure-Aware
Line Art from SPS (Drawing)
19/35

20. Experimental Images - Bird Category
Tone-based
Secord’s Stippling Method (Secord)
20/35

21. Experimental Images - Bird Category
Tone-based
Line Art using edge tangent field (Mmosaics)
21/35

22. Experimental Images - Bird Category
Contrast-Aware Halftoning (CAH)
22/35

23. Experimental Images - Bird Category
Black and White (BW)
23/35

24. Experimental Images - Bird Category
Reduced Information
Adding 50% salt and pepper noise (Noisy)
24/35

25. Experimental Images - Bird Category
Reduced Information
Gaussian filter (Blurring)
25/35

26. Positive Correlation Between Structural and Aesthetic Ratings
26/35

27. Dot-based Methods (Stippling)
Tone-based
Structure-Aware
Structure-Aware
Tone-based
27/35

28. Region-based Methods (Mosaics)
Tone-based
Structure-Aware
Structure-Aware
Tone-based
28/35

29. Effect of Category on Ratings
29/35

30. Effect of Category on Response Time
Building < Flower < Bird < Cat < Person < Mug < Car
30/35

31. Artists and Non-Artists
31/35

32. Overall Ranks after Study
Participants preferred the AT images (7/30 responses) the most, CAH second (6/30).
Participants’ least favorite
blurred images most often (20/30 responses), and with AT second (5/30).
Controversial ranking for stylized images rendered by the AT method.
32/35

33. Conclusions
Considering structure as a possible way to increase aesthetic appeal.
Considering the choice of the images used.
Generally, bird images were the easiest images to abstract, while Person images were the most difficult.
33/35

34. Future Work More Participants More Categories More NPR Algorithms
Eye tracker
34/35

35. Thanks for Your Attention.
Questions?
35/35

36. Effect of Algorithms on Ratings (skip)
36/41

37. Effect of Algorithms on Response Time (skip)
37/41

38. Interaction between Categories and Algorithms (skip)
Aesthetic scores
38/41

39. Interaction between Categories and Algorithms (skip)
39/41

40. Interaction between Categories and Algorithms (skip)
40/41