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The Efficiency of Perceptual Quality Metrics 3D Meshes Based on the Human Visual System

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Soft Computing Applications (SOFA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 633))

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Abstract

The representation of content as a 3D mesh is a very emerging technology. These three-dimensional meshes can be a scan of objects, characters or 3D scenes. Mesh quality is a determining factor in treatment of effectiveness, accuracy of results and rendering quality. You can show users these 3D meshes with a texture on the 3D mesh surface. The estimated quality by an observer is a very complex task related to the complexity of the Human Visual System (HVS). In this paper we present the efficiency of perceptual quality metrics 3D meshes based on the human visual system.

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References

  1. Lecuire, V., Duran-Faundez, C., Krommenacker, N.: Energy-efficient image transmission in sensor networks. Int. J. Sens. Netw. 4(1/2), 37–47 (2008)

    Article  Google Scholar 

  2. Salehpour, M., Behrad, A.: 3D face reconstruction by KLT feature extraction and model consistency match refining and growing. In: 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), Sousse, pp. 297–302 (2012)

    Google Scholar 

  3. Chan, A.T., Gamino, A.: Integration of assistive technologies into 3D simulations: an exploratory study. In: Information Technology: New Generations, Advances in Intelligent Systems and Computing, vol. 448, pp. 425–437. Springer (2016). ISBN 978-3-319-18295-7

    Google Scholar 

  4. El-Bendary, M.A.M., El-Tokhy, M., Kazemian, H.B.: Efficient image transmission over low-power IEEE802.15.1 network over correlated fading channels. In: The 6th International Conferences: Sciences of Electronics, Technologies of Information and Telecommunications “SETIT 2012”, Mars 2012, Sousse-Tunisie, IEEE Conferences, pp. 563–567 (2012). doi:10.1109/SETIT.2012.6481973

  5. Abderrahim, Z., Techini, E., Bouhlel, M.S.: State of the art: compression of 3D meshes. Int. J. Comput. Trends Technol. (IJCTT) 4(6), 765–770 (2012)

    Google Scholar 

  6. Tang, H., Joshi, N., Kapoor, A.: Learning a blind measure of perceptual image quality. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, pp. 305–312 (2011)

    Google Scholar 

  7. Hemanth, D.J., Balas, V.E., Anitha, J.: Hybrid neuro-fuzzy approaches for abnormality detection in retinal images. In: Proceedings of the 6th International Workshop Soft Computing Applications, SOFA 2014, Timisoara, Romania, 24–26 July 2014, pp. 295–305 (2014)

    Google Scholar 

  8. El-Bendary, M.A.M., El-Tokhy, M., Shawki, F., Abd-El-Samie, F.E.: Studying the throughput efficiency of JPEG image transmission over mobile IEEE 802.15.1 network using EDR packets. In: The 6th International Conferences: Sciences of Electronics, Technologies of Information and Telecommunications “SETIT 2012”, Mars 2012, Sousse-Tunisie, IEEE Conferences, pp. 573–577 (2012). doi:10.1109/SETIT.2012.6481975

  9. Escribano-Barreno, J., García-Muñoz, J.: Integrated metrics handling in open source software quality management platforms. In: Information Technology: New Generations. Advances in Intelligent Systems and Computing, vol. 448, pp. 509–518. Springer (2016). ISBN 978-3-319-18295-7

    Google Scholar 

  10. Triki, N., Kallel, M., Bouhlel, M.S.: Imaging and HMI, fondations and complementarities. In: The 6th International Conferences: Sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2012, Mars 2012, Sousse-Tunisie, IEEE Conferences, pp. 25–29 (2012). doi:10.1109/SETIT.2012.6481884

  11. Nandi, D., Ashour, A.S., Samanta, S., Chakraborty, S., Salem, M.A., Dey, N.: Principal component analysis in medical image processing: a study. Int. J. Image Min. 1(1), 65–86 (2015)

    Article  Google Scholar 

  12. Cho, J.-W., Prost, R., Jung, H.-Y.: An oblivious watermarking for 3-D polygonal meshes using distribution of vertex norms. IEEE Trans. Sig. Process. 55(1), 142–155 (2007)

    Article  MathSciNet  Google Scholar 

  13. Wang, K., Lavoué, G., Denis, F., Baskurt, A.: Robustand blind mesh watermarking based on volume moments. Comput. Graph. 35(1), 1–19 (2011)

    Article  Google Scholar 

  14. Abderrahim, Z., Techini, E., Bouhlel, M.S.: Progressive compression of 3D objects with an adaptive quantization. Int. J. Comput. Sci. Issues (IJCSI) 10(2), 504–511 (2013)

    Google Scholar 

  15. Campbell, F.-W., Robson, J.-G.: Application of Fourier analysis to the visibility of gratings. J. Physiol. 197, 551–566 (1968)

    Article  Google Scholar 

  16. Hirai, K., Tsumura, N., Nakaguchi, T., Miyake, Y., Tominaga, S.: Spatio-velocity contrast sensitivity functions and video quality assessment. In: International Symposium on Intelligent Signal Processing and Communication Systems, pp. 1–4 (2010)

    Google Scholar 

  17. Ninassi, A., Meur, O.L., Le Callet, P., Barba, D.: On the performance of human visual system based image quality assessment metric using wavelet Domain. In: Proceedings of the SPIE Human Vision and Electronic Imaging (2008)

    Google Scholar 

  18. Ninassi, A., Meur, O.L., Le Callet, P., Barba, D.: Which semi-local visual masking model for wavelet based image quality metric In: Proceedings of IEEE International Conference on Image Processing, pp. 1180–1183 (2008)

    Google Scholar 

  19. Fernandez-Maloigne, C., Larabi, M.-C., Bringier, B., Richard, N.: Spatio temporal characteristics of the human color perception for digital quality assessment. In: Proceedings of International Symposium on Signals, Circuits and Systems, pp. 203–206 (2005)

    Google Scholar 

  20. Kelly, D.-H.: Visual contrast sensitivity. Opt. Acta: Int. J. Opt. 24(2), 107–129 (1977)

    Article  Google Scholar 

  21. Daly, S.-J.: Engineering observations from spatio velocity and spatio temporal visual models. In: Proceedings of the SPIE Human Vision and Electronic Imaging III, vol. 3299, pp. 180–191 (1998)

    Google Scholar 

  22. Wang, Z., Bovik, A.-C.: Modern Image Quality Assessment. Morgan & Claypool, San Rafael (2006)

    Google Scholar 

  23. Rogowitz, B.-E., Rushmeier, H.-E.: Are image quality metrics adequate to evaluate the quality of geometric objects. In: Proceedings of SPIE Human Vision and Electronic Imaging, pp. 340–348 (2001)

    Google Scholar 

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

    Article  Google Scholar 

  25. Lavoué, G.: A multiscale metric for 3D mesh visual quality assessment. Comput. Graph. Forum 30(5), 1427–1437 (2011)

    Article  Google Scholar 

  26. Lavoué, G., Drelie Gelasca, E., Dupont, F., Baskurt, A., Ebrahimi, T.: Perceptually driven 3D distance metrics with application to watermarking. In: Proceedings of SPIE Electronic Imaging (2006)

    Google Scholar 

  27. Wang, Z., Bovik, A.-C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  28. Alliez, P., Tayeb, S., Wormser, C.: 3D fast intersection and distance computation (AABB tree). In: CGAL User and Reference Manual (2012)

    Google Scholar 

  29. Wu, J.-H., Hu, S.-M., Tai, C.-L., Sun, J.-G.: An effective feature-preserving mesh simplification scheme based on face constriction. In: Pacific Conference on Computer Graphics and Applications, pp. 12–21 (2001)

    Google Scholar 

  30. Gelasca, E.-D., Ebrahimi, T.: Objective evaluation of the perceptual quality of 3D watermarking. In: IEEE International Conference on Image Processing, pp. 241–244 (2005)

    Google Scholar 

  31. Corsini, M., Gelasca, E.-D., Ebrahimi, T.: A multi-scale roughness metric for 3D watermarking quality assessment. In: Workshop on Image Analysis for Multimedia Interactive Services (2005)

    Google Scholar 

  32. 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 

  33. Vása, L., Rus, J.: Dihedral angle mesh error: a fast perception correlated distortion measure for fixed connectivity triangle meshes. Comput. Graph. Forum 31(5), 1715–1724 (2012)

    Article  Google Scholar 

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Correspondence to Nessrine Elloumi .

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Elloumi, N., Loukil Hadj Kacem, H., Bouhlel, M.S. (2018). The Efficiency of Perceptual Quality Metrics 3D Meshes Based on the Human Visual System. In: Balas, V., Jain, L., Balas, M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-319-62521-8_43

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  • DOI: https://doi.org/10.1007/978-3-319-62521-8_43

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