A Comparative Study of the 3D Quality Metrics: Application to Masking Database

High definition and 3D telemedicine offer a compelling mechanism to achieve a sense of immersion and contribute to an enhanced quality of use. 3D mesh perceptual quality is crucial for many applications. Although there exist some objective metrics for measuring distances between meshes, they do not integrate the characteristics of the human visual system and thus are unable to predict the visual quality.

[1]  Jean-Marc Chassery,et al.  A Curvature-Tensor-Based Perceptual Quality Metric for 3D Triangular Meshes , 2012, Machine Graphics and Vision.

[2]  Hocine Cherifi,et al.  Reduced Reference 3D Mesh Quality Assessment Based on Statistical Models , 2015, 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[3]  A. Masmoudi,et al.  Image encryption using chaotic standard map and engle continued fractions map , 2012, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).

[4]  Jeremy R. Cooperstock,et al.  Multimodal Telepresence Systems , 2011, IEEE Signal Processing Magazine.

[5]  T. Ebrahimi,et al.  Watermarked 3-D Mesh Quality Assessment , 2007, IEEE Transactions on Multimedia.

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  Hans-Peter Seidel,et al.  Perception-guided global illumination solution for animation rendering , 2001, SIGGRAPH.

[8]  Andrew B. Watson,et al.  Digital images and human vision , 1993 .

[9]  Touradj Ebrahimi,et al.  Objective evaluation of the perceptual quality of 3D watermarking , 2005, IEEE International Conference on Image Processing 2005.

[10]  Guillaume Lavoué,et al.  A local roughness measure for 3D meshes and its application to visual masking , 2009, TAP.

[11]  Massimiliano Corsini,et al.  A Comparison of Perceptually-Based Metrics for Objective Evaluation of Geometry Processing , 2010, IEEE Transactions on Multimedia.

[12]  Nilanjan Dey,et al.  Rough Set Based Ad Hoc Network: A Review , 2014, Int. J. Serv. Sci. Manag. Eng. Technol..

[13]  A. Khalfallah,et al.  Evaluation of image fusion techniques in nuclear medicine , 2012, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).

[14]  Paolo Cignoni,et al.  Metro: Measuring Error on Simplified Surfaces , 1998, Comput. Graph. Forum.

[15]  Ali Khalfallah,et al.  Image encryption with dynamic chaotic Look-Up Table , 2012, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).

[16]  Craig Gotsman,et al.  Spectral compression of mesh geometry , 2000, EuroCG.

[17]  Shi-Min Hu,et al.  An effective feature-preserving mesh simplification scheme based on face constriction , 2001, Proceedings Ninth Pacific Conference on Computer Graphics and Applications. Pacific Graphics 2001.

[18]  Don Brutzman,et al.  X3D: extensible 3D graphics standard , 2008, SIGGRAPH 2008.

[19]  M. S. Bouhlel,et al.  Imaging and HMI: Fondations and complementarities , 2012, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).

[20]  Touradj Ebrahimi,et al.  Perceptually driven 3D distance metrics with application to watermarking , 2006, SPIE Optics + Photonics.

[21]  D. Brutzman,et al.  X3D: Extensible 3D Graphics Standard [Standards in a Nutshell] , 2007, IEEE Signal Processing Magazine.

[22]  Touradj Ebrahimi,et al.  MESH: measuring errors between surfaces using the Hausdorff distance , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[23]  Libor Vása,et al.  Perceptual Metrics for Static and Dynamic Triangle Meshes , 2013, Eurographics.

[24]  Jonathan D. Cohen,et al.  Level of Detail for 3D Graphics , 2012 .