Skip to main content
SpringerLink
Log in

Visual Mesh Quality Assessment Using Weighted Network Representation

  • Conference paper
  • First Online:
Complex Networks & Their Applications XII (COMPLEX NETWORKS 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1141))

Included in the following conference series:

  • 991 Accesses

Abstract

This paper addresses the critical task of evaluating the visual quality of triangular mesh models. We introduce an innovative approach that leverages weighted graphs for this purpose. Motivated by the growing need for accurate quality assessment in various fields, including computer graphics and 3D modeling, our methodology begins by generating saliency maps for each distorted mesh model. These models are subsequently transformed into a network representation, where mesh vertices are nodes and mesh edges are edges in the graph. The determination of vertex weights relies on the salience values. We then extract a wide range of topological properties and compute statistical measures to create a signature vector. To predict the quality score, we rigorously evaluate the performance of three regression algorithms. Experiments span four publicly available databases designed for mesh model quality assessment. Results demonstrate that the proposed approach excels in this task, showcasing remarkable correlations with subjective evaluations. This preliminary analysis paves the way for further research to address potential limitations and explore additional applications of mesh network representation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Pastrana-Vidal, R.R., Gicquel, J.-C., Colomes, C., Cherifi, H.: Frame dropping effects on user quality perception. In: Proceedings of 5th International WIAMIS (2004)

    Google Scholar 

  2. Pastrana-Vidal, R.R., Gicquel, J.C., Blin, J.L., Cherifi, H.: Predicting subjective video quality from separated spatial and temporal assessment. SPIE Human Vision Electron. Imaging XI 6057, 276–286 (2006)

    Google Scholar 

  3. Corsini, M., Larabi, M.-C., Lavoué, G., Petrik, O., Vasa, L., Wang, K.: Perceptual metrics for static and dynamic triangle meshes. In: Computer Graphics Forum, vol. 32, no. 1, pp. 101–125. Wiley Online Library (2013)

    Google Scholar 

  4. Cignoni, P., Rocchini, C., Scopigno, R.: Metro: measuring error on simplified surfaces. In: Computer Graphics Forum, vol. 17, pp. 167–174. Wiley Online Library (1998)

    Google Scholar 

  5. Aspert, N., Santa-Cruz, D., Ebrahimi, T.: Mesh: measuring errors between surfaces using the Hausdorff distance. In: IEEE International Conference on. Multimedia and Expo, vol. 1, pp. 705–708 (2002)

    Google Scholar 

  6. Lavoué, G.: A multiscale metric for 3d mesh visual quality assessment. In: Computer Graphics Forum, vol. 30, pp. 1427–1437. Wiley Online Library (2011)

    Google Scholar 

  7. Torkhani, F., Wang, K., Chassery, J.M.: A curvature tensor distance for mesh visual quality assessment. In: Computer Vision and Graphics, pp. 253–263 (2012)

    Google Scholar 

  8. Corsini, M., Gelasca, E.D., Ebrahimi, T., Barni, M.: Watermarked 3-d mesh quality assessment. IEEE Trans. Multimedia 9(2), 247–256 (2007)

    Article  Google Scholar 

  9. Wang, K., Torkhani, F., Montanvert, A.: A fast roughness-based approach to the assessment of 3D mesh visual quality. Comput. Graph. 36(7), 808–818 (2012)

    Article  Google Scholar 

  10. Váša, L., Rus, J.: Dihedral angle mesh error: a fast perception correlated distortion measure for fixed connectivity triangle meshes. In: Computer Graphics Forum, vol. 31, no. 5, pp. 1715–1724. Blackwell Publishing Ltd., Oxford (2012)

    Google Scholar 

  11. Abouelaziz, I., El Hassouni, M., Cherifi, H.: A convolutional neural network framework for blind mesh visual quality assessment. In: IEEE International Conference on Image Processing (ICIP), pp. 755–759 (2017)

    Google Scholar 

  12. Abouelaziz, I., Chetouani, A., El Hassouni, M., Latecki, L.J., Cherifi, H.: Convolutional neural network for blind mesh visual quality assessment using 3d visual saliency. In: 25th IEEE International Conference on Image Processing (ICIP), pp. 3533–3537 (2018)

    Google Scholar 

  13. Hamidi, M., Chetouani, A., El Haziti, M., El Hassouni, M., Cherifi, H.: Blind robust 3D mesh watermarking based on mesh saliency and wavelet transform for copyright protection. Information 10(2), 67 (2019)

    Article  Google Scholar 

  14. Abouelaziz, I., Chetouani, A., El Hassouni, M., Latecki, L.J., Cherifi, H.: No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling. Pattern Recogn. 100, 107174 (2020)

    Article  Google Scholar 

  15. Mourchid, Y., El Hassouni, M., Cherifi, H.: A general framework for complex network-based image segmentation. Multimedia Tools Appl. 78, 20191–20216 (2019)

    Article  Google Scholar 

  16. Rital, S., Bretto, A., Cherifi, H., Aboutajdine, D.: A combinatorial edge detection algorithm on noisy images. In: International IEEE Symposium on VIPromCom Video/Image Processing and Multimedia Communications, pp. 351–355 (2002)

    Google Scholar 

  17. Rital, S., Cherifi, H., Miguet, S.: Weighted adaptive neighborhood hypergraph partitioning for image segmentation. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) Pattern Recognition and Image Analysis: Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, 22–25 August 2005, Proceedings, Part II, vol. 3, pp. 522–531. Springer, Heidelberg (2005). https://doi.org/10.1007/11552499_58

  18. Ribas, L.C., Riad, R., Jennane, R., Bruno, O.M.: A complex network-based approach for knee Osteoarthritis detection: data from the Osteoarthritis initiative. Biomed. Signal Process. Control. 71, 103133 (2022)

    Article  Google Scholar 

  19. Lasfar, A., Mouline, S., Aboutajdine, D., Cherifi, H.: Content-based retrieval in fractal coded image databases. In: Proceedings 15th International Conference on Pattern Recognition, vol. 1, pp. 1031–1034. IEEE ICPR (2000)

    Google Scholar 

  20. Demirkesen, C., Cherifi, H.: A comparison of multiclass SVM methods for real-world natural scenes. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008, vol. 5259. Springer, Heidelberg (2008)

    Google Scholar 

  21. Hassouni, M.E., Cherifi, H., Aboutajdine, D.: HOS-based image sequence noise removal. IEEE Trans. Image Process. 15(3), 572–581 (2006)

    Article  Google Scholar 

  22. Lin, Y., Yu, M., Chen, K., Jiang, G., Chen, F., Peng, Z.: Blind mesh assessment based on graph spectral entropy and spatial features. Entropy 22(2), 190 (2020)

    Article  MathSciNet  Google Scholar 

  23. Abouelaziz, I., Chetouani, A., Hassouni, M.E., Cherifi, H., Latecki, L.J.: Learning graph convolutional network for blind mesh visual quality assessment. IEEE Access 9, 108200–108211 (2021)

    Article  Google Scholar 

  24. Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. In: ACM SIGGRAPH 2005 Papers, pp. 659–666 (2005)

    Google Scholar 

  25. Lavoué, G., Gelasca, E.D., Dupont, F., Baskurt, A., Ebrahimi, T.: Perceptually driven 3D distance metrics with application to watermarking. In: Applications of Digital Image Processing XXIX, vol. 6312, p. 63120L. International Society for Optics and Photonics (2006)

    Google Scholar 

  26. Lavoué, G., Larabi, M.C., Váša, L.: On the efficiency of image metrics for evaluating the visual quality of 3D models. IEEE Trans. Visualization Comput. Graph. 22(8), 1987–1999 (2015)

    Article  Google Scholar 

  27. Silva, S., Santos, B.S., Ferreira, C., Madeira, J.: A perceptual data repository for polygonal meshes. In: 2009 Second International Conference in Visualisation, pp. 207–212. IEEE (2009)

    Google Scholar 

  28. Abouelaziz, I., El Hassouni, M., Cherifi, H.: No-reference 3d mesh quality assessment based on dihedral angles model and support vector regression. In: Mansouri, A., Nouboud, F., Chalifour, A., Mammass, D., Meunier, J., Elmoataz, A. (eds.) International Conference on Image and Signal Processing, pp. 369–377. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33618-3_37

  29. Abouelaziz, I., El Hassouni, M., Cherifi, H.: A curvature based method for blind mesh visual quality assessment using a general regression neural network. In: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 793–797. IEEE (2016)

    Google Scholar 

Download references

Acknowledgment

This work is partially supported by the research grant of the Hassan II Academy of Sciences and Technology of Morocco.

Author information

Authors and Affiliations

  1. FLSH, Mohammed V University in Rabat, Rabat, Morocco

    Mohammed El Hassouni

  2. ICB UMR 6303 CNRS, University of Burgundy, Dijon, France

    Hocine Cherifi

Authors
  1. Mohammed El Hassouni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hocine Cherifi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed El Hassouni .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Thomas J. Watson College of Engineering and Applied Sciences, Binghamton University, Binghamton, NY, USA

    Luis M. Rocha

  3. IUT Lumière - Université Lyon 2, University of Lyon, Bron, France

    Chantal Cherifi

  4. Department of Economics, Yildiz Technical University, Istanbul, Türkiye

    Murat Donduran

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

El Hassouni, M., Cherifi, H. (2024). Visual Mesh Quality Assessment Using Weighted Network Representation. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1141. Springer, Cham. https://doi.org/10.1007/978-3-031-53468-3_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-53468-3_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53467-6

  • Online ISBN: 978-3-031-53468-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation