A Multiscale Metric for 3D Mesh Visual Quality Assessment

Many processing operations are nowadays applied on 3D meshes like compression, watermarking, remeshing and so forth; these processes are mostly driven and/or evaluated using simple distortion measures like the Hausdorff distance and the root mean square error, however these measures do not correlate with the human visual perception while the visual quality of the processed meshes is a crucial issue. In that context we introduce a full‐reference 3D mesh quality metric; this metric can compare two meshes with arbitrary connectivity or sampling density and produces a score that predicts the distortion visibility between them; a visual distortion map is also created. Our metric outperforms its counterparts from the state of the art, in term of correlation with mean opinion scores coming from subjective experiments on three existing databases. Additionally, we present an application of this new metric to the improvement of rate‐distortion evaluation of recent progressive compression algorithms.

[1]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[2]  Qing Zhu,et al.  Quantitative analysis of discrete 3D geometrical detail levels based on perceptual metric , 2010, Comput. Graph..

[3]  Bruce Walter,et al.  Visual equivalence: towards a new standard for image fidelity , 2007, ACM Trans. Graph..

[4]  Pierre Alliez,et al.  Anisotropic polygonal remeshing , 2003, ACM Trans. Graph..

[5]  Greg Turk,et al.  Image-driven simplification , 2000, TOGS.

[6]  Bernice E. Rogowitz,et al.  Are image quality metrics adequate to evaluate the quality of geometric objects? , 2001, IS&T/SPIE Electronic Imaging.

[7]  Sivan Toledo,et al.  High-Pass Quantization for Mesh Encoding , 2003, Symposium on Geometry Processing.

[8]  Scott J. Daly,et al.  Visible differences predictor: an algorithm for the assessment of image fidelity , 1992, Electronic Imaging.

[9]  David W. Jacobs,et al.  Mesh saliency , 2005, ACM Trans. Graph..

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

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

[12]  Gary W. Meyer,et al.  Perceptually Guided Polygon Reduction , 2008, IEEE Transactions on Visualization and Computer Graphics.

[13]  Rémy Prost,et al.  An Oblivious Watermarking for 3-D Polygonal Meshes Using Distribution of Vertex Norms , 2007, IEEE Transactions on Signal Processing.

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

[15]  Hans-Peter Seidel,et al.  Video quality assessment for computer graphics applications , 2010, SIGGRAPH 2010.

[16]  Gabriel Taubin,et al.  Geometric Signal Processing on Polygonal Meshes , 2000, Eurographics.

[17]  Ann McNamara,et al.  Perceptually-motivated graphics, visualization and 3D displays , 2010, SIGGRAPH '10.

[18]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

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

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

[21]  Beatriz Sousa Santos,et al.  A Perceptual Data Repository for Polygonal Meshes , 2009, 2009 Second International Conference in Visualisation.

[22]  Gary W. Meyer,et al.  A perceptually based adaptive sampling algorithm , 1998, SIGGRAPH.

[23]  C.-C. Jay Kuo,et al.  Geometry-guided progressive lossless 3D mesh coding with octree (OT) decomposition , 2005, ACM Trans. Graph..

[24]  Rémy Prost,et al.  Wavelet-based progressive compression scheme for triangle meshes: wavemesh , 2004, IEEE Transactions on Visualization and Computer Graphics.

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

[26]  Sheila S. Hemami,et al.  VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images , 2007, IEEE Transactions on Image Processing.

[27]  Atilla Baskurt,et al.  Robust and blind mesh watermarking based on volume moments , 2011, Comput. Graph..

[28]  Rémy Prost,et al.  Progressive Lossless Mesh Compression Via Incremental Parametric Refinement , 2009, Comput. Graph. Forum.

[29]  Peter Lindstrom,et al.  Evaluation of Memoryless Simplification , 1999, IEEE Trans. Vis. Comput. Graph..

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

[31]  Zhou Wang,et al.  Modern Image Quality Assessment , 2006, Modern Image Quality Assessment.

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

[33]  Martin Reddy,et al.  Perceptually Optimized 3D Graphics , 2001, IEEE Computer Graphics and Applications.

[34]  David Cohen-Steiner,et al.  Restricted delaunay triangulations and normal cycle , 2003, SCG '03.

[35]  Václav Skala,et al.  A Perception Correlated Comparison Method for Dynamic Meshes , 2011, IEEE Transactions on Visualization and Computer Graphics.

[36]  Ralph R. Martin,et al.  Evaluation for Small Visual Difference Between Conforming Meshes on Strain Field , 2009, Journal of Computer Science and Technology.