Subjective and Objective Visual Quality Assessment of Textured 3D Meshes

Abstract

Objective visual quality assessment of 3D models is a fundamental issue in computer graphics. Quality assessment metrics may allow a wide range of processes to be guided and evaluated, such as level of detail creation, compression, filtering, and so on. Most computer graphics assets are composed of geometric surfaces on which several texture images can be mapped to make the rendering more realistic. While some quality assessment metrics exist for geometric surfaces, almost no research has been conducted on the evaluation of texture-mapped 3D models. In this context, we present a new subjective study to evaluate the perceptual quality of textured meshes, based on a paired comparison protocol. We introduce both texture and geometry distortions on a set of 5 reference models to produce a database of 136 distorted models, evaluated using two rendering protocols. Based on analysis of the results, we propose two new metrics for visual quality assessment of textured mesh, as optimized linear combinations of accurate geometry and texture quality measurements. These proposed perceptual metrics outperform their counterparts in terms of correlation with human opinion. The database, along with the associated subjective scores, will be made publicly available online.

References

1. Tunç Ozan Aydın, Martin Čadík, Karol Myszkowski, and Hans-Peter Seidel. 2010. Video quality assessment for computer graphics applications. ACM Transactions on Graphics 29, 6 (Dec 2010), 1. DOI:http://dx.doi.org/10.1145/1882261.1866187
2. Martin Čadík, Robert Herzog, Rafal Mantiuk, Radosaw Mantiuk, Karol Myszkowski, and Hans-Peter Seidel. 2013. Learning to predict localized distortions in rendered images. In Pacific Graphics, Vol. 32.
3. Martin Čadík, Robert Herzog, Rafal Mantiuk, Karol Myszkowski, and Hans-Peter Seidel. 2012. New measurements reveal weaknesses of image quality metrics in evaluating graphics artifacts. ACM Transactions on Graphics 31, 6 (2012), Article 147.
4. D. M. Chandler and S. S. Hemami. 2007. VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Transactions on Image Processing 16, 9 (Sep 2007), 2284--2298.
5. I. Cheng and A. Basu. 2007. Perceptually optimized 3-D transmission over wireless networks. IEEE Transactions on Multimedia 9, 2 (Feb 2007), 386--396. DOI:http://dx.doi.org/10.1109/TMM.2006.886291
6. Massimiliano Corsini, Elisa Drelie Gelasca, Touradj Ebrahimi, and Mauro Barni. 2007. Watermarked 3-D mesh quality assessment. IEEE Transactions on Multimedia 9, 2 (Feb 2007), 247--256.
7. M. Corsini, M. C. Larabi, G. Lavoué, O. Petrik, L. Váša, and K. Wang. 2013. Perceptual metrics for static and dynamic triangle meshes. Computer Graphics Forum 32, 1 (Feb 2013), 101--125.
8. Scott Daly. 1993. The visible differences predictor: An algorithm for the assessment of image fidelity. In Digital Images and Human Vision, Andrew B. Watson (Ed.). MIT Press, Cambridge, 179--206.
9. J. Ferwerda, S. Pattanaik, P. Shirley, and D. Greenberg. 1997. A model of visual masking for computer graphics. In ACM SIGGRAPH. 143--152.
10. M. Garland and P.-S. Heckbert. 1997. Surface simplification using quadric error metrics. In ACM SIGGRAPH. 209--216.
11. Z. Govindarajulu, M. Kendall, and J. D. Gibbons. 1992. Rank correlation methods. Technometrics 34, 1 (Feb 1992), 108. DOI:http://dx.doi.org/10.2307/1269571
12. Wesley Griffin and Marc Olano. 2015. Evaluating texture compression masking effects using objective image quality assessment metrics. IEEE Transactions on Visualization and Computer Graphics 21, 8 (2015), 970--079. DOI:http://dx.doi.org/10.1109/TVCG.2015.2429576
13. Jinjiang Guo, Vincent Vidal, Atilla Baskurt, and Guillaume Lavoué. 2015. Evaluating the local visibility of geometric artifacts. In Proceedings of the ACM Symposium in Applied Perception.
14. Robert Herzog, Martin Čadík, Tunç O. Aydın, Kwang In Kim, Karol Myszkowski, and Hans-P. Seidel. 2012. NoRM: No-reference image quality metric for realistic image synthesis. Computer Graphics Forum 31, 2 (Pt 3), 545--554. DOI:http://dx.doi.org/10.1111/j.1467-8659.2012.03055.x
15. Z. Karni and C. Gotsman. 2000. Spectral compression of mesh geometry. In ACM Siggraph. 279--286.
16. Guillaume Lavoué. 2011. A multiscale metric for 3D mesh visual quality assessment. Computer Graphics Forum 30, 5 (2011), 1427--1437.
17. G. Lavoué, M. C. Larabi, and Libor Váša. 2016. On the efficiency of image metrics for evaluating the visual quality of 3D models. IEEE Transactions on Visualization and Computer Graphics 22, 8 (2016), 1987--1999. DOI:http://dx.doi.org/10.1109/TVCG.2015.2480079
18. Guillaume Lavoué and Rafa Mantiuk. 2015. Quality assessment in computer graphics. Visual Signal Quality Assessment: Quality of Experience (QoE) (2015), 243--286. DOI:http://dx.doi.org/10.1007/978-3-319-10368-6_9
19. Patrick Ledda, Alan Chalmers, Tom Troscianko, and Helge Seetzen. 2005. Evaluation of tone mapping operators using a high dynamic range display. ACM Transactions on Graphics 24, 3 (Jul 2005), 640.
20. Jeffrey Lubin. 1993. The use of psychophysical data and models in the analysis of display system performance. In Digital Images and Human Vision, A. B. Watson (Ed.). 163--178.
21. J. Mannos and D. Sakrison. 1974. The effects of a visual fidelity criterion of the encoding of images. IEEE Transactions on Information Theory 20, 4 (Jul 1974), 525--536. DOI:http://dx.doi.org/10.1109/tit.1974.1055250
22. Rafal Mantiuk, Kil Joong Kim, Allan G. Rempel, and Wolfgang Heidrich. 2011. HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. In ACM Transactions on Graphics (Proc. of SIGGRAPH'11) 30, 4 (2011), Article no. 40.
23. Rafa K. Mantiuk, Anna Tomaszewska, and Radosaw Mantiuk. 2012. Comparison of four subjective methods for image quality assessment. Computer Graphics Forum 31, 8 (Dec 2012), 2478--2491. DOI:http://dx.doi.org/10.1111/j.1467-8659.2012.03188.x
24. Georges Nader, Kai Wang, H. Franck, and Florent Dupont. 2016. Just noticeable distortion profile for flat-shaded 3D mesh surfaces. IEEE Transactions on Visualization and Computer Graphics (2016). DOI:http://dx.doi.org/10.1109/TVCG.2015.2507578
25. J. P. O’Shea, M. S. Banks, and M. Agrawala. 2008. The assumed light direction for perceiving shape from shading. In Proceedings of the Symposium on Applied Perception in Graphics and Visualization.
26. Yixin Pan, Irene Cheng, and Anup Basu. 2005. Quality metric for approximating subjective evaluation of 3-D objects. IEEE Transactions on Multimedia 7, 2 (Apr 2005), 269--279.
27. Lijun Qu and G. W. Meyer. 2008. Perceptually guided polygon reduction. IEEE Transactions on Visualization and Computer Graphics 14, 5 (2008), 1015--1029. DOI:http://dx.doi.org/10.1109/TVCG.2008.51
28. Bernice E. Rogowitz and H. Rushmeier. 2001. Are image quality metrics adequate to evaluate the quality of geometric objects? In Proceedings of SPIE. 340--348.
29. Kalpana Seshadrinathan, Rajiv Soundararajan, Alan Conrad Bovik, and Lawrence K. Cormack. 2010. Study of subjective and objective quality assessment of video. IEEE Transactions on Image Processing 19, 6 (Jun 2010), 1427--41. DOI:http://dx.doi.org/10.1109/TIP.2010.2042111
30. H. R. Sheikh and A. C. Bovik. 2006. Image information and visual quality. IEEE Transactions on Image Processing 15, 2 (Feb 2006), 430--444.
31. D. Amnon Silverstein and Joyce E. Farrell. 2001. Efficient method for paired comparison. Journal of Electronic Imaging 10, 2 (2001), 394. DOI:http://dx.doi.org/10.1117/1.1344187
32. J. Ström and T. Akenine-Möller. 2005. i PACKMAN: High-quality, low-complexity texture compression for mobile phones. In Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware. 177--182. DOI:http://dx.doi.org/10.1145/1071866.1071877
33. Jennifer Sun and Pietro Perona. 1998. Where is the sun? Nature Neuroscience (1998), 183--184. Retrieved from http://www.nature.com/neuro/journal/v1/n3/abs/nn0798.
34. G. Taubin. 1995. A signal processing approach to fair surface design. In ACM Siggraph. 351--358.
35. L. L. Thurstone. 1927. A law of comparative judgments. Psychological Review 34 (1927), 273--286.
36. Dihong Tian and Ghassan AlRegib. 2004. FQM. In Proceedings of the 12th Annual ACM International Conference on Multimedia - MULTIMEDIA 04. Association for Computing Machinery (ACM). DOI:http://dx.doi.org/10.1145/1027527.1027684
37. Dihong Tian and G. AlRegib. 2008. Batex3: Bit allocation for progressive transmission of textured 3-D models. IEEE Transactions on Circuits and Systems for Video Technology 18, 1 (2008), 23--35.
38. Fakhri Torkhani, Kai Wang, and Jean-Marc Chassery. 2012. A curvature tensor distance for mesh visual quality assessment. In Proceedings of the International Conference on Computer Vision and Graphics.
39. Libor Váša and Jan Rus. 2012. Dihedral angle mesh error: A fast perception correlated distortion measure for fixed connectivity triangle meshes. Computer Graphics Forum 31, 5 (2012), 1715--1724.
40. Kai Wang, Fakhri Torkhani, and Annick Montanvert. 2012. A fast roughness-based approach to the assessment of 3D mesh visual quality. Computers 8 Graphics 36, 7 (2012), 808--818.
41. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (Apr 2004), 600--612. DOI:http://dx.doi.org/10.1109/tip.2003.819861
42. Zhou Wang and Alan C. Bovik. 2006. Modern Image Quality Assessment. Vol. 2. Morgan 8 Claypool Publishers. DOI:http://dx.doi.org/10.2200/S00010ED1V01Y200508IVM003
43. Zhou Wang and Qiang Li. 2011. Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing 20, 5 (May 2011), 1185--1198. DOI:http://dx.doi.org/10.1109/tip.2010.2092435
44. Z. Wang, E. P. Simoncelli, and A. C. Bovik. 2003. Multiscale structural similarity for image quality assessment. IEEE Asilomar Conference on Signals, Systems and Computers 2, 1 (2003), 1398--1402. DOI:http://dx.doi.org/10.1109/ACSSC.2003.1292216
45. Benjamin Watson, Alinda Friedman, and Aaron McGaffey. 2001. Measuring and predicting visual fidelity. In ACM SIGGRAPH. 213--220.
46. Feng Xiao. 2000. DCT-based Video Quality Evaluation. Technical Report. Stanford University. Retrieved from http://compression.ru/video/quality.
47. Sheng Yang, Chao-Hua Lee, and C. C. J. Kuo. 2004. Optimized mesh and texture multiplexing for progressive textured model transmission. In Proceedings of the ACM Multimedia Conference. 676--683. DOI:http://dx.doi.org/10.1145/1027527.1027683
48. Hojatollah Yeganeh and Zhou Wang. 2013. Objective quality assessment of tone-mapped images. IEEE Transactions on Image Processing 22, 2 (Feb 2013), 657--67. DOI:http://dx.doi.org/10.1109/TIP.2012.2221725
49. L. Zhang. 2012. A comprehensive evaluation of full reference image quality assessment algorithms. In Proceedings of the International Conference on Image Processing (ICIP). 1477--1480.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision representations

        • Appearance and texture representations

  • Computer graphics

    • Graphics systems and interfaces

      • Perception

    • Shape modeling

      • Mesh models