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...
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| Published in | Computer graphics forum Vol. 30; no. 5; pp. 1427 - 1437 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Oxford, UK
Blackwell Publishing Ltd
01.08.2011
Wiley |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. [PUBLICATION ABSTRACT] 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. |
| Author | Lavoué, Guillaume |
| Author_xml | – sequence: 1 givenname: Guillaume surname: Lavoue fullname: Lavoue, Guillaume |
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| References | Corsini M., Gelasca E. D., Ebrahimi T., Barni M.: Watermarked 3-D Mesh Quality Assessment. IEEE Transactions on Multimedia 9, 2 (2007), 247-256. 2, 3, 8, 9. Valette S., Chaine R., Prost R.: Progressive lossless mesh compression via incremental parametric refinement. In Eurographics Symposium on Geometry processing (2009), vol. 28, pp. 1301-1310. 9. Wang Z., Bovik A. C.: Modern Image Quality Assessment, vol. 2. Morgan & Claypool Publishers, 2006. 2. Valette S., Prost R.: A wavelet-based progressive compression scheme for triangle meshes: Wavemesh. IEEE Transactions on Visualization and Computer Graphics 10, 2 (2004), 123-129. 9. Lavoué G., Corsini M.: A comparison of perceptually-based metrics for objective evaluation of geometry processing. IEEE Transactions on Multimedia 12, 7 (2010), 636-649. 1, 3, 8. Lindstrom P., Turk G.: Evaluation of memoryless simplification. IEEE Transactions on Visualization and Computer Graphics 5, 2 (1999), 98-115. 3. Wang Z., Bovik A., Sheikh H., Simoncelli E.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image processing 13, 4 (2004), 600-612. 2, 3, 5, 7. Lavoue G., Drelie Gelasca E., Dupont F., Baskurt A., Ebrahimi T.: Perceptually driven 3D distance metrics with application to watermarking. In proceedings of SPIE (2006), vol. 6312, pp. 63120L-63120L-12. 1, 2, 3, 4, 5, 8, 9. Lindstrom P., Turk G.: Image Driven Simplification. ACM Transactions on Graphics 19, 3 (2000), 204-241. 2. Wang K., Lavoué G., Denis F., Baskurt A.: Robust and blind mesh watermarking based on volume moments. Computers & Graphics 35, 1 (2011), 1-19. 3, 6, 7. Bian Z., Hu S., Martin R.: Evaluation for small visual difference between conforming meshes on strain field. Journal of Computer Science and Technology 24 (2009), 65-75. 2. Wang Z., Simoncelli E., Bovik A.: Multiscale structural similarity for image quality assessment. IEEE Asilomar Conference on Signals, Systems and Computers 2, 1 (2003), 1398-1402. 2, 4. Peng J., Kuo C.-C. J.: Geometry-guided progressive lossless 3D mesh coding with octree (OT) decomposition. ACM Transactions on Graphics 24, 3 (2005). 9. Zhu Q., Zhao J., Du Z., Zhang Y.: Quantitative analysis of discrete 3D geometrical detail levels based on perceptual metric. Computers & Graphics 34, 1 (2010), 55-65. 2, 3. Cho J., Prost R., Jung H.: An oblivious watermarking for 3-D polygonal meshes using distribution of vertex norms. IEEE Transactions on Signal processing 55 (2006), 142-155. 3. Chandler D., Hemami S.: VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Transactions on Image processing 16, 9 (2007), 2284-2298. 2. Lavoué G.: A local roughness measure for 3D meshes and its application to visual masking. ACM Transactions on Applied perception (TAP) 5, 4 (2009). 2, 4, 5, 8. Taubin G.: Geometric signal processing on polygonal meshes. Eurographics State of the Art Reports 4 (2000). 3. Váša L., Skala V.: A perception correlated comparison method for dynamic meshes. IEEE transactions on visualization and computer graphics 17, 2 (2011), 220-30. 2. Zhang L., Mou X., Zhang D.: FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Transactions on Image processing, 99 (2011), 1-1. 2. Aydin T. O., ČAdík M., Myszkowski K., Seidel H.-P.: Video quality assessment for computer graphics applications. ACM Transactions on Graphics 29, 6 (2010), 1. 2. Reddy M.: Perceptually optimized 3D graphics. IEEE Computer Graphics and Applications 21, 5 (2001), 68-75. 2. Daly S.: The visible differences predictor: an algorithm for the assessment of image fidelity. MIT Press, Cambridge , 1993, pp. 179-206. 2. Alliez P., Cohen-Steiner D., Devillers O., Lévy B., Desbrun M.: Anisotropic polygonal remeshing. ACM Transactions on Graphics 22, 3 (2003), 485. 4. Qu L., Meyer G.: Perceptually guided polygon reduction. IEEE Transactions on Visualization and Computer Graphics 14, 5 (2008), 1015-1029. 2. Cignoni P., Rocchini C., Scopigno R.: Metro: Measuring Error on Simplified Surfaces. Computer Graphics Forum 17, 2 (1998), 167-174. 8. 2010; 12 2010; 34 2009; 24 2010 2000; 4 2006; 55 2009 1998 2008; 14 2007 2011; 99 2005 2011; 35 1993 2003 2006; 2 2011; 17 1999; 5 2005; 24 2007; 16 2001; 21 2009; 28 2004; 10 1998; 17 2000; 19 2006; 6312 2001 2000 2010; 29 2004; 13 2003; 2 2007; 9 2009; 5 2003; 22 Taubin G. (e_1_2_9_30_2) 2000; 4 e_1_2_9_10_2 e_1_2_9_33_2 e_1_2_9_34_2 Ramanarayanan G. (e_1_2_9_26_2) 2007 e_1_2_9_11_2 e_1_2_9_32_2 Wang Z. (e_1_2_9_37_2) 2003; 2 e_1_2_9_14_2 e_1_2_9_13_2 e_1_2_9_35_2 e_1_2_9_15_2 e_1_2_9_36_2 e_1_2_9_18_2 e_1_2_9_17_2 e_1_2_9_39_2 e_1_2_9_19_2 e_1_2_9_21_2 e_1_2_9_20_2 e_1_2_9_23_2 e_1_2_9_22_2 e_1_2_9_7_2 Zhang L. (e_1_2_9_38_2) 2011; 99 e_1_2_9_6_2 Valette S. (e_1_2_9_31_2) 2009; 28 e_1_2_9_5_2 e_1_2_9_4_2 e_1_2_9_3_2 Lavoué G. (e_1_2_9_16_2) 2009; 5 e_1_2_9_2_2 Daly S. (e_1_2_9_12_2) 1993 e_1_2_9_9_2 e_1_2_9_8_2 e_1_2_9_25_2 e_1_2_9_24_2 e_1_2_9_27_2 e_1_2_9_29_2 e_1_2_9_28_2 |
| References_xml | – reference: Lavoué G., Corsini M.: A comparison of perceptually-based metrics for objective evaluation of geometry processing. IEEE Transactions on Multimedia 12, 7 (2010), 636-649. 1, 3, 8. – reference: Váša L., Skala V.: A perception correlated comparison method for dynamic meshes. IEEE transactions on visualization and computer graphics 17, 2 (2011), 220-30. 2. – reference: Zhang L., Mou X., Zhang D.: FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Transactions on Image processing, 99 (2011), 1-1. 2. – reference: Wang K., Lavoué G., Denis F., Baskurt A.: Robust and blind mesh watermarking based on volume moments. Computers & Graphics 35, 1 (2011), 1-19. 3, 6, 7. – reference: Valette S., Chaine R., Prost R.: Progressive lossless mesh compression via incremental parametric refinement. In Eurographics Symposium on Geometry processing (2009), vol. 28, pp. 1301-1310. 9. – reference: Wang Z., Simoncelli E., Bovik A.: Multiscale structural similarity for image quality assessment. IEEE Asilomar Conference on Signals, Systems and Computers 2, 1 (2003), 1398-1402. 2, 4. – reference: Daly S.: The visible differences predictor: an algorithm for the assessment of image fidelity. MIT Press, Cambridge , 1993, pp. 179-206. 2. – reference: Lavoue G., Drelie Gelasca E., Dupont F., Baskurt A., Ebrahimi T.: Perceptually driven 3D distance metrics with application to watermarking. In proceedings of SPIE (2006), vol. 6312, pp. 63120L-63120L-12. 1, 2, 3, 4, 5, 8, 9. – reference: Wang Z., Bovik A., Sheikh H., Simoncelli E.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image processing 13, 4 (2004), 600-612. 2, 3, 5, 7. – reference: Bian Z., Hu S., Martin R.: Evaluation for small visual difference between conforming meshes on strain field. Journal of Computer Science and Technology 24 (2009), 65-75. 2. – reference: Lavoué G.: A local roughness measure for 3D meshes and its application to visual masking. ACM Transactions on Applied perception (TAP) 5, 4 (2009). 2, 4, 5, 8. – reference: Wang Z., Bovik A. C.: Modern Image Quality Assessment, vol. 2. Morgan & Claypool Publishers, 2006. 2. – reference: Reddy M.: Perceptually optimized 3D graphics. IEEE Computer Graphics and Applications 21, 5 (2001), 68-75. 2. – reference: Zhu Q., Zhao J., Du Z., Zhang Y.: Quantitative analysis of discrete 3D geometrical detail levels based on perceptual metric. Computers & Graphics 34, 1 (2010), 55-65. 2, 3. – reference: Valette S., Prost R.: A wavelet-based progressive compression scheme for triangle meshes: Wavemesh. IEEE Transactions on Visualization and Computer Graphics 10, 2 (2004), 123-129. 9. – reference: Lindstrom P., Turk G.: Evaluation of memoryless simplification. IEEE Transactions on Visualization and Computer Graphics 5, 2 (1999), 98-115. 3. – reference: Corsini M., Gelasca E. D., Ebrahimi T., Barni M.: Watermarked 3-D Mesh Quality Assessment. IEEE Transactions on Multimedia 9, 2 (2007), 247-256. 2, 3, 8, 9. – reference: Aydin T. O., ČAdík M., Myszkowski K., Seidel H.-P.: Video quality assessment for computer graphics applications. ACM Transactions on Graphics 29, 6 (2010), 1. 2. – reference: Taubin G.: Geometric signal processing on polygonal meshes. Eurographics State of the Art Reports 4 (2000). 3. – reference: Cho J., Prost R., Jung H.: An oblivious watermarking for 3-D polygonal meshes using distribution of vertex norms. IEEE Transactions on Signal processing 55 (2006), 142-155. 3. – reference: Cignoni P., Rocchini C., Scopigno R.: Metro: Measuring Error on Simplified Surfaces. Computer Graphics Forum 17, 2 (1998), 167-174. 8. – reference: Qu L., Meyer G.: Perceptually guided polygon reduction. IEEE Transactions on Visualization and Computer Graphics 14, 5 (2008), 1015-1029. 2. – reference: Alliez P., Cohen-Steiner D., Devillers O., Lévy B., Desbrun M.: Anisotropic polygonal remeshing. ACM Transactions on Graphics 22, 3 (2003), 485. 4. – reference: Lindstrom P., Turk G.: Image Driven Simplification. ACM Transactions on Graphics 19, 3 (2000), 204-241. 2. – reference: Peng J., Kuo C.-C. J.: Geometry-guided progressive lossless 3D mesh coding with octree (OT) decomposition. ACM Transactions on Graphics 24, 3 (2005). 9. – reference: Chandler D., Hemami S.: VSNR: A wavelet-based visual signal-to-noise ratio for natural images. 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| Snippet | Many processing operations are nowadays applied on 3D meshes like compression, watermarking, remeshing and so forth; these processes are mostly driven and/or... |
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| SubjectTerms | Compressing Compression tests Computer graphics Computer Science Correlation analysis Data compression Density Distortion I.3.5 [Computer Graphics]: Computer Graphics-Computational Geometry and Object Modeling I.3.m [Computer Graphics]: Computer Graphics-Perception Mapping Studies Three dimensional Visual Watermarking |
| Title | A Multiscale Metric for 3D Mesh Visual Quality Assessment |
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