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Quality Assessment in Computer Graphics

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Visual Signal Quality Assessment

Abstract

In this chapter, we review the existing works regarding visual quality assessment in computer graphics. This broad area of research includes many sub-domains which make intensive use of quality assessment and/or artifact visibility evaluation: geometry processing, rendering, HDR imaging, tone-map**, and stereo vision. For each of these sub-domains, we present the existing objective quality metrics, the subjective quality experiments as well as an evaluation and comparison of their performance. We broadly classify these existing works into image-based (i.e., evaluating artifacts introduced in 2D rendered images and videos) and model-based approaches (i.e., artifacts introduced on the 3D models themselves). Finally, the last part presents the emerging trends and main future directions.

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Notes

  1. 1.

    http://vcg.isti.cnr.it/activities/surfacegrevis/simplification/metro.html.

  2. 2.

    http://liris.cnrs.fr/guillaume.lavoue/data/datasets.html.

  3. 3.

    http://www.ieeta.pt/~sss/repository/.

  4. 4.

    http://compression.kiv.zcu.cz/.

References

  1. Akyüz, A.O., Fleming, R., Riecke, B.E., Reinhard, E., Bulthoff, H.H.: Do HDR displays support LDR content? A psychophysical evaluation. ACM Transactions on Graphics 26(3), article no. 38 (2007)

    Google Scholar 

  2. Akyüz, A.O., Reinhard, E.: Perceptual evaluation of tone reproduction operators using the Cornsweet-Craik-O’Brien illusion. ACM Transactions on Applied Perception 4(4), 1–29 (2008)

    Article  Google Scholar 

  3. Allan, R., Terry, M.E.: Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons. Biometrica 39(3/4), 324–345 (1952)

    MATH  Google Scholar 

  4. Ashikhmin, M., Goyal, J.: A reality check for tone map** operators. ACM Transactions on Applied Perception 3(4), 399–411 (2006)

    Article  Google Scholar 

  5. Aydın, T.O., Mantiuk, R., Myszkowski, K., Seidel, H.P.: Dynamic range independent image quality assessment. ACM Transactions on Graphics (Proc. of SIGGRAPH) 27(3), 69 (2008)

    Google Scholar 

  6. Aydın, T.O., Mantiuk, R., Seidel, H.P.: Extending quality metrics to full luminance range images. In: Proceedings of SPIE, pp. 68,060B–10. Spie (2008). DOI 10.1117/12.765095

  7. Aydın, T.O., Čadík, M., Myszkowski, K., Seidel, H.P.: Video quality assessment for computer graphics applications. ACM Transactions on Graphics 29(6), 1 (2010). DOI 10.1145/1882261.1866187

    Article  Google Scholar 

  8. Banterle, F., Ledda, P., Debattista, K., Bloj, M., Artusi, A., Chalmers, A.: A Psychophysical Evaluation of Inverse Tone Map** Techniques. Computer Graphics Forum 28(1), 13–25 (2009). DOI 10.1111/j.1467-8659.2008.01176.x

    Article  Google Scholar 

  9. Blackwell, H.: Contrast thresholds of the human eye. Journal of the Optical Society of America 36(11), 624–632 (1946)

    Article  Google Scholar 

  10. Boitard, R., Cozot, R., Thoreau, D., Bouatouch, K.: Temporal coherency in video tone map**, a survey. In: HDRi2013 - First International Conference and SME Workshop on HDR imaging, Xx, p. no. 1 (2013)

    Google Scholar 

  11. Bolin, M.R., Meyer, G.W.: A frequency based ray tracer. In: Proc. of SIGGRAPH ’95, pp. 409–418 (1995)

    Google Scholar 

  12. Bolin, M.R., Meyer, G.W.: A perceptually based adaptive sampling algorithm. In: Proceedings of the 25th annual conference on Computer graphics and interactive techniques - SIGGRAPH ’98, pp. 299–309. ACM Press, New York, New York, USA (1998). DOI 10.1145/280814.280924

  13. Bulbul, A., Capin, T., Lavoue, G., Preda, M.: Measuring Visual Quality of 3D Polygonal Models. IEEE Signal Processing Magazine 28(6), 80–90 (2011)

    Article  Google Scholar 

  14. Cater, K., Chalmers, A., Ward, G.: Detail to Attention: Exploiting Visual Tasks for Selective Rendering. Proc. of Eurographics workshop on Rendering pp. 270–280 (2003)

    Google Scholar 

  15. 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(1), 142–155 (2007)

    Article  MathSciNet  Google Scholar 

  16. Cignoni, P., Rocchini, C., Scopigno, R.: Metro: Measuring Error on Simplified Surfaces. Computer Graphics Forum 17(2), 167–174 (1998). DOI 10.1111/1467-8659.00236

    Article  Google Scholar 

  17. Cleju, I., Saupe, D.: Evaluation of supra-threshold perceptual metrics for 3D models. In: Symposium on Applied Perception in Graphics and Visualization. ACM Press (2006). DOI 10.1145/1140491.1140499

    Google Scholar 

  18. Corsini, M., Gelasca, E.D., Ebrahimi, T., Barni, M.: Watermarked 3-D Mesh Quality Assessment. IEEE Transactions on Multimedia 9(2), 247–256 (2007)

    Article  Google Scholar 

  19. Corsini, M., Larabi, M.C., Lavoué, G., Petík, O., Váša, L., Wang, K.: Perceptual Metrics for Static and Dynamic Triangle Meshes. Computer Graphics Forum 32(1), 101–125 (2013). DOI 10.1111/cgf.12001

    Article  Google Scholar 

  20. Daly, S.: The Visible Differences Predictor: An Algorithm for the Assessment of Image Fidelity. In: A.B. Watson (ed.) Digital Images and Human Vision, pp. 179–206. MIT Press (1993)

    Google Scholar 

  21. Delahunt, P.B., Zhang, X., Brainard, D.H.: Perceptual image quality: Effects of tone characteristics. Journal of Electronic Imaging 14(2), 1–12 (2005). DOI 10.1117/1.1900134

    Google Scholar 

  22. Drago, F., L. Martens, W., Myszkowski, K., Sidel, H.P.: Perceptual Evaluation of Tone Map** Operators with Regard to Similarity and Preference. Tech. rep., MPI Informatik (2002)

    Google Scholar 

  23. Dumont, R., Pellacini, F., Ferwerda, J.A.: Perceptually-driven decision theory for interactive realistic rendering. ACM Transactions on Graphics 22(2), 152–181 (2003). DOI 10.1145/636886.636888

    Article  Google Scholar 

  24. Eilertsen, G., Wanat, R., Mantiuk, R.K., Unger, J.: Evaluation of Tone Map** Operators for HDR-Video. Computer Graphics Forum (Proc. of Pacific Graphics) 32(7), 275–284 (2013)

    Google Scholar 

  25. Engeldrum, P.: Psychometric scaling: a toolkit for imaging systems development. Imcotek Press (2000)

    Google Scholar 

  26. Ferwerda, J.A., Shirley, P., Pattanaik, S.N., Greenberg, D.P.: A model of visual masking for computer graphics. In: Proc. of SIGGRAPH ’97, pp. 143–152. ACM Press, New York, New York, USA (1997). DOI 10.1145/258734.258818

  27. Georgeson, M.A., Sullivan, G.D.: Contrast constancy: deblurring in human vision by spatial frequency channels. J. Physiol. 252(3), 627–656 (1975)

    Article  Google Scholar 

  28. Guthe, M., Müller, G., Schneider, M., Klein, R.: BTF-CIELab: A Perceptual Difference Measure for Quality Assessment and Compression of BTFs. Computer Graphics Forum 28(1), 101–113 (2009). DOI 10.1111/j.1467-8659.2008.01299.x

    Article  Google Scholar 

  29. Hadziabdic, K.K., Telalovic, J.H., Mantiuk, R.: Comparison of deghosting algorithms for multi-exposure high dynamic range imaging. In: Proc. of Spring Conference on Computer Graphics, pp. 1–8 (2013)

    Google Scholar 

  30. Herzog, R., Čadík, M., Aydın, T.O., Kim, K.I., Myszkowski, K., Seidel, H.P.: NoRM: No-Reference Image Quality Metric for Realistic Image Synthesis. Computer Graphics Forum 31(2pt3), 545–554 (2012). DOI 10.1111/j.1467-8659.2012.03055.x

  31. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998). DOI 10.1109/34.730558

    Article  Google Scholar 

  32. Karni, Z., Gotsman, C.: Spectral compression of mesh geometry. In: ACM Siggraph, pp. 279–286 (2000)

    Google Scholar 

  33. Karni, Z., Gotsman, C.: Compression of Soft-Body animation sequences. Computers & Graphics 28(1), 25–34 (2004)

    Article  Google Scholar 

  34. Kelly, D.H.: Motion and vision. {II}. Stabilized spatiotemporal threshold surface. Journal of Optical Society of America 69(10), 1340–1349 (1979)

    Google Scholar 

  35. Kim, K.J., Mantiuk, R., Lee, K.H.: Measurements of achromatic and chromatic contrast sensitivity functions for an extended range of adaptation luminance. In: B.E. Rogowitz, T.N. Pappas, H. de Ridder (eds.) Human Vision and Electronic Imaging, p. 86511A (2013). DOI 10.1117/12.2002178

  36. Korshunov, P., Ebrahimi, T.: Influence of Context and Content on Tone-map** Operators. In: HDRi2013 - First International Conference and SME Workshop on HDR imaging, p. no. 2 (2013)

    Google Scholar 

  37. Krawczyk, G., Myszkowski, K., Seidel, H.P.: Contrast Restoration by Adaptive Countershading. Computer Graphics Forum 26(3), 581–590 (2007). DOI 10.1111/j.1467-8659.2007.01081.x

    Article  Google Scholar 

  38. Kuang, J., Heckaman, R., Fairchild, M.D.: Evaluation of HDR tone-map** algorithms using a high-dynamic-range display to emulate real scenes. Journal of the Society for Information Display 18(7), 461–468 (2010). DOI 10.1889/JSID18.7.461

    Article  Google Scholar 

  39. Kuang, J., Yamaguchi, H., Johnson, G.M., Fairchild, M.D.: Testing HDR image rendering algorithms. In: Proc. IS&T/SID 12th Color Imaging Conference, pp. 315–320. Scotsdale, Arizona (2004)

    Google Scholar 

  40. Larkin, M., O’Sullivan, C.: Perception of Simplification Artifacts for Animated Characters. In: symposium on Applied perception in graphics and visualization, pp. 93–100 (2011)

    Google Scholar 

  41. Lavoué, G.: A local roughness measure for 3D meshes and its application to visual masking. ACM Transactions on Applied Perception (TAP) 5(4) (2009)

    Google Scholar 

  42. Lavoué, G.: A Multiscale Metric for 3D Mesh Visual Quality Assessment. Computer Graphics Forum 30(5), 1427–1437 (2011)

    Article  Google Scholar 

  43. Lavoué, G., Cheng, I., Basu, A.: Perceptual Quality Metrics for 3D Meshes: Towards an Optimal Multi-Attribute Computational Model. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2013)

    Google Scholar 

  44. Lavoué, G., Corsini, M.: A comparison of perceptually-based metrics for objective evaluation of geometry processing. IEEE Transactions on Multimedia 12(7), 636–649 (2010)

    Article  Google Scholar 

  45. Lavoue, G., Drelie Gelasca, E., Dupont, F., Baskurt, A., Ebrahimi, T.: Perceptually driven 3D distance metrics with application to watermarking. In: SPIE, vol. 6312, pp. 63,120L–63,120L–12. SPIE (2006)

    Google Scholar 

  46. Ledda, P., Chalmers, A., Troscianko, T., Seetzen, H.: Evaluation of tone map** operators using a high dynamic range display. ACM Transactions on Graphics 24(3), 640–648 (2005)

    Article  Google Scholar 

  47. Lindstrom, P.: Model Simplification using Image and Geometry-Based Metrics. Ph.D. thesis, Georgia Institute of Technology (2000)

    Google Scholar 

  48. Lindstrom, P., Turk, G.: Evaluation of memoryless simplification. IEEE Transactions on Visualization and Computer Graphics 5(2), 98–115 (1999). DOI 10.1109/2945.773803

    Article  Google Scholar 

  49. Lindstrom, P., Turk, G.: Image Driven Simplification. ACM Transactions on Graphics 19(3), 204–241 (2000)

    Article  Google Scholar 

  50. Liu, Y., Wang, J., Cho, S., Finkelstein, A., Rusinkiewicz, S.: A no-reference metric for evaluating the quality of motion deblurring. ACM Transactions on Graphics 32(6), 1–12 (2013). DOI 10.1145/2508363.2508391

    Google Scholar 

  51. Lubin, J.: A visual discrimination model for imaging system design and evaluation. In: E. Peli (ed.) Vision Models for Target Detection and Recognition, pp. 245–283. World Scientific Publishing Company (1995)

    Google Scholar 

  52. Luebke, D., Hallen, B.: Perceptually driven simplification for interactive rendering. In: Rendering Techniques 2001: Proceedings of the Eurographics Workshop, p. 223 (2001)

    Google Scholar 

  53. Luebke, D., Hallen, B., Newfield, D., Watson, B.: Perceptually Driven Simplification Using Gaze-Directed Rendering. In: EGSR, pp. 223–234 (2001)

    Google Scholar 

  54. Mantiuk, R., Daly, S., Myszkowski, K., Seidel, H.: Predicting visible differences in high dynamic range images: model and its calibration. In: Human Vision and Electronic Imaging, pp. 204–214 (2005)

    Google Scholar 

  55. Mantiuk, R., Kim, K.J., Rempel, A.G., Heidrich, W.: HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans. Graph (Proc. SIGGRAPH) 30(4), 1 (2011). DOI 10.1145/2010324.1964935

  56. Mantiuk, R.K., Tomaszewska, A., Mantiuk, R.: Comparison of four subjective methods for image quality assessment. Computer Graphics Forum 31(8), 2478–2491 (2012)

    Article  Google Scholar 

  57. Masia, B., Agustin, S., Fleming, R.W., Sorkine, O., Gutierrez, D.: Evaluation of reverse tone map** through varying exposure conditions. ACM Transactions on Graphics 28(5), 1 (2009). DOI 10.1145/1618452.1618506

    Article  Google Scholar 

  58. McCann, J., Rizzi, A.: Veiling glare: the dynamic range limit of hdr images. In: Proc. of HVEI XII, vol. 6492, pp. 649,213–649,213. International Society for Optics and Photonics (2007)

    Google Scholar 

  59. McCann, J.J., Rizzi, A.: The Art and Science of HDR Imaging (Google eBook). John Wiley & Sons (2011)

    Google Scholar 

  60. Menzel, N., Guthe, M.: Towards Perceptual Simplification of Models with Arbitrary Materials. Computer Graphics Forum 29(7), 2261–2270 (2010). DOI 10.1111/j.1467-8659.2010.01815.x

    Article  Google Scholar 

  61. Mullen, K.T.: The contrast sensitivity of human colour vision to red-green and blue-yellow chromaic gratings. Journal of Physiolohy 359, 381–400 (1985)

    Article  Google Scholar 

  62. Myszkowski, K.: The visible differences predictor: Applications to global illumination problems. In: Rendering techniques’ 98: proceedings of the Eurographics Workshop in Vienna, Austria, June 29-July 1, 1998, p. 223. Springer Verlag Wien (1998)

    Google Scholar 

  63. Myszkowski, K., Rokita, P., Tawara, T.: Perceptually-informed accelerated rendering of high quality walkthrough sequences. In: Eurographics Workshop on Rendering, vol. 99, pp. 5–18 (1999)

    Google Scholar 

  64. Myszkowski, K., Tawara, T., Akamine, H., Seidel, H.P.: Perception-guided global illumination solution for animation rendering. In: Proc. of SIGGRAPH’01, pp. 221–230. ACM, New York, NY, USA (2001). DOI 10.1145/383259.383284

  65. O’Donovan, P., Agarwala, A., Hertzmann, A.: Color compatibility from large datasets. ACM Transactions on Graphics 30(4), 1 (2011). DOI 10.1145/2010324.1964958

    Article  Google Scholar 

  66. O’Sullivan, C., Howlett, S., Morvan, Y.: Perceptually adaptive graphics. Eurographics State of the Art Reports pp. 141–164 (2004)

    Google Scholar 

  67. Pan, Y., Cheng, I., Basu, A.: Quality metric for approximating subjective evaluation of 3-D objects. IEEE Transactions on Multimedia 7(2), 269–279 (2005)

    Article  Google Scholar 

  68. Petit, J., Mantiuk, R.K.: Assessment of video tone-map**: Are cameras S-shaped tone-curves good enough? Journal of Visual Communication and Image Representation 24, 1020–1030 (2013). DOI 10.1016/j.jvcir.2013.06.014

    Article  Google Scholar 

  69. Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008 - A database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics 10, 30–45 (2009)

    Google Scholar 

  70. Qu, L., Meyer, G.: Perceptually guided polygon reduction. IEEE Transactions on Visualization and Computer Graphics 14(5), 1015–1029 (2008). DOI 10.1109/TVCG.2008.51

    Article  Google Scholar 

  71. Ramanarayanan, G., Ferwerda, J., Walter, B.: Visual equivalence: towards a new standard for image fidelity. ACM Transactions on Graphics (TOG) 26(3), 76 (2007). DOI 10.1145/1276377.1276472

  72. Ramasubramanian, M., Pattanaik, S.N., Greenberg, D.P.: A perceptually based physical error metric for realistic image synthesis. In: Proceedings of the 26th annual conference on Computer graphics and interactive techniques - SIGGRAPH ’99, pp. 73–82. ACM Press, New York, New York, USA (1999). DOI 10.1145/311535.311543

  73. Reddy, M.: SCROOGE: Perceptually-Driven Polygon Reduction. Computer Graphics Forum 15(4), 191–203 (1996)

    Article  Google Scholar 

  74. Rogowitz, B.E., Holly E. Rushmeier: Are image quality metrics adequate to evaluate the quality of geometric objects? Proceedings of SPIE pp. 340–348 (2001)

    Google Scholar 

  75. Rubinstein, M., Gutierrez, D., Sorkine, O., Shamir, A.: A comparative study of image retargeting. ACM Transactions on Graphics 29(6), 1 (2010). DOI 10.1145/1882261.1866186

    Article  Google Scholar 

  76. Rushmeier, H., Rogowitz, B., Piatko, C.: Perceptual issues in substituting texture for geometry. In: SPIE, pp. 372–383. International Society for Optical Engineering; 1999 (2000)

    Google Scholar 

  77. Schroeder, W., Zarge, J., Lorensen, W.: Decimation of triangle meshes. In: ACM Siggraph, pp. 65–70 (1992)

    Google Scholar 

  78. Secord, A., Lu, J., Finkelstein, A., Singh, M., Nealen, A.: Perceptual models of viewpoint preference. ACM Transactions on Graphics 30(5), 1–12 (2011). DOI 10.1145/2019627.2019628

    Article  Google Scholar 

  79. Silva, S., Santos, B., Ferreira, C.: Comparison of methods for the simplification of mesh models using quality indices and an observer study. SPIE pp. 64,921L–64,921L–12 (2007)

    Google Scholar 

  80. Silva, S., Santos, B.S., Ferreira, C., Madeira, J.: A Perceptual Data Repository for Polygonal Meshes. 2009 Second International Conference in Visualisation pp. 207–212 (2009)

    Google Scholar 

  81. Silverstein, D., Farrell, J.: Efficient method for paired comparison. Journal of Electronic Imaging 10, 394 (2001). DOI 10.1117/1.1344187

    Article  Google Scholar 

  82. Smith, K., Krawczyk, G., Myszkowski, K.: Beyond tone map**: Enhanced depiction of tone mapped HDR images. Computer Graphics Forum 25(3), 427–438 (2006)

    Article  Google Scholar 

  83. Sorkine, O., Cohen-Or, D., Toldeo, S.: High-pass quantization for mesh encoding. In: Eurographics Symposium on Geometry Processing, pp. 42–51 (2003)

    Google Scholar 

  84. Tian, D., AlRegib, G.: FQM: A Fast Quality Measure for Efficient Transmission of Textured 3D Models. In: ACM Multimedia, pp. 684–691 (2004)

    Google Scholar 

  85. Torkhani, F., Wang, K., Chassery, J.m.: A Curvature Tensor Distance for Mesh Visual Quality Assessment. In: International Conference on Computer Vision and Graphics (2012)

    Google Scholar 

  86. Trentacoste, M., Mantiuk, R., Heidrich, W., Dufrot, F.: Unsharp Masking, Countershading and Halos: Enhancements or Artifacts? Computer Graphics Forum 31(2pt3), 555–564 (2012). DOI 10.1111/j.1467-8659.2012.03056.x

    Google Scholar 

  87. Vasa, L., Skala, V.: A Perception Correlated Comparison Method for Dynamic Meshes. IEEE Trans. on Visualization and Computer Graphics 17(2), 220–230 (2011)

    Article  Google Scholar 

  88. Váša, L., Rus, J.: Dihedral Angle Mesh Error: a fast perception correlated distortion measure for fixed connectivity triangle meshes. Computer Graphics Forum 31(5) (2012)

    Google Scholar 

  89. Čadík, M., Herzog, R., Mantiuk, R., Mantiuk, R., Myszkowski, K., Seidel, H.P.: Learning to Predict Localized Distortions in Rendered Images. Computer Graphics Forum (Proc. of Pacific Graphics) 32(7), 401–410 (2013)

    Google Scholar 

  90. Čadík, M., Herzog, R., Mantiuk, R.K., Myszkowski, K., Seidel, H.P., Čadík, M.: New Measurements Reveal Weaknesses of Image Quality Metrics in Evaluating Graphics Artifacts. ACM Trans. Graph (Proc. SIGGRAPH Asia) 31(6), 147 (2012). DOI 10.1145/2366145.2366166

  91. Čadík, M., Wimmer, M., Neumann, L., Artusi, A.: Evaluation of HDR tone map** methods using essential perceptual attributes. Computers & Graphics 32(3), 330–349 (2008). DOI 10.1016/j.cag.2008.04.003

    Article  Google Scholar 

  92. Villa, C., Labayrade, R.: Psychovisual assessment of tone-map** operators for global appearance and colour reproduction. In: Proc. of Colour in Graphics Imaging and Vision 2010, pp. 189–196. Joensuu, Finland (2010)

    Google Scholar 

  93. VQEG: Final report from the video quality experts group on the validation of objective models of video quality assessment. Tech. rep., Video Quality Experts Group (2000)

    Google Scholar 

  94. Walter, B., Pattanaik, S.N., Greenberg, D.P.: Using Perceptual Texture Masking for Efficient Image Synthesis. Computer Graphics Forum 21(3), 393–399 (2002). DOI 10.1111/1467-8659.t01-1-00599

    Article  Google Scholar 

  95. Wang, K., Lavoué, G., Denis, F., Baskurt, A.: Robust and blind mesh watermarking based on volume moments. Computers & Graphics 35(1), 1–19 (2011)

    Article  MATH  Google Scholar 

  96. Wang, K., Torkhani, F., Montanvert, A.: A Fast Roughness-Based Approach to the Assessment of 3D Mesh Visual Quality. Computers & Graphics (2012)

    Google Scholar 

  97. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004)

    Article  Google Scholar 

  98. Watson, A., Ahumada Jr, A.: A standard model for foveal detection of spatial contrast. Journal of Vision 5(9), 717–740 (2005)

    Article  Google Scholar 

  99. Watson, A.B.: The cortex transform: Rapid computation of simulated neural images. Computer Vision, Graphics, and Image Processing 39(3), 311–327 (1987). DOI 10.1016/S0734-189X(87)80184-6

    Article  Google Scholar 

  100. Watson, B., Friedman, A., McGaffey, A.: Measuring and predicting visual fidelity. ACM Siggraph pp. 213–220 (2001)

    Google Scholar 

  101. Williams, N., Luebke, D., Cohen, J., Kelley, M., Schubert, B.: Perceptually Guided Simplification of Lit, Textured Meshes. In: ACM Symposium on Interactive 3D Graphics, pp. 113–121 (2003)

    Google Scholar 

  102. Wilson, H.R.: A transducer function for threshold and suprathreshold human vision. Biological Cybernetics 38(3), 171–178 (1980). DOI 10.1007/BF00337406

    Article  Google Scholar 

  103. Yee, H.: Perceptual Metric for Production Testing. Journal of Graphics Tools 9(4), pages 33–40 (2004)

    Article  MathSciNet  Google Scholar 

  104. Yee, H., Pattanaik, S., Greenberg, D.P.: Spatiotemporal sensitivity and visual attention for efficient rendering of dynamic environments. ACM Transactions on Graphics 20(1), 39–65 (2001). DOI 10.1145/383745.383748

    Article  Google Scholar 

  105. Yeganeh, H., Wang, Z.: Objective quality assessment of tone-mapped images. IEEE Transactions on Image Processing 22(2), 657–67 (2013). DOI 10.1109/TIP.2012.2221725

    Article  MathSciNet  Google Scholar 

  106. Yoshida, A., Blanz, V., Myszkowski, K., Seidel, H.P.: Perceptual evaluation of tone map** operators with real world scenes. In: Proc. of SPIE Human Vision and Electronic Imaging X, vol. 5666, pp. 192–203. San Jose, CA (2005)

    Google Scholar 

  107. Yoshida, A., Mantiuk, R., Myszkowski, K., Seidel, H.P.: Analysis of reproducing real-world appearance on displays of varying dynamic range. Computer Graphics Forum 25(3), 415–426 (2006)

    Article  Google Scholar 

  108. Zhang, X., Wandell, B.A.: A spatial extension of CIELAB for digital color-image reproduction. Journal of the Society for Information Display 5(1), 61 (1997). DOI 10.1889/1.1985127

    Article  Google Scholar 

  109. Zhu, Q., Zhao, J., Du, Z., Zhang, Y.: Quantitative analysis of discrete 3D geometrical detail levels based on perceptual metric. Computers & Graphics 34(1), 55–65 (2010). DOI 10.1016/j.cag.2009.10.004

    Article  Google Scholar 

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Authors and Affiliations

  1. CNRS, University of Lyon, Insa-Lyon, LIRIS UMR 5205, Lyon, France

    Guillaume Lavoué

  2. School of Computer Science, Bangor University, Bangor, Gwynedd, LL57 2DG, UK

    Rafał Mantiuk

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  1. School of Information and Electronics, Bei**g Institute of Technology, Bei**g, China

    Chenwei Deng

  2. Huawei Noah’s Ark Lab, Quarry Bay, Hong Kong SAR

    Lin Ma

  3. School of Computer Engineering, Nanyang Technological University, Singapore

    Weisi Lin

  4. Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR

    King Ngi Ngan

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Lavoué, G., Mantiuk, R. (2015). Quality Assessment in Computer Graphics. In: Deng, C., Ma, L., Lin, W., Ngan, K. (eds) Visual Signal Quality Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-10368-6_9

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