D. S. Taubman and M. W. Marcellin, JPEG2000 Image Compression Fundamentals, Standards and Practice, 2002.
DOI : 10.1007/978-1-4615-0799-4

J. Strömstr¨ström and T. Akenine-m-¨-oller, i PACKMAN: high-quality, lowcomplexity texture compression for mobile phones, ACM SIG- GRAPH/EUROGRAPHICS Conference on Graphics Hardware, pp.177-182, 2005.

J. Nystad, A. Lassen, A. Pomianowski, S. Ellis, and T. Olson, Adaptive scalable texture compression, ACM SIGGRAPH / Eurographics conference on High-Performance Graphics, pp.105-114, 2012.

P. Krajcevski, FasTC, Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D '13, 2013.
DOI : 10.1145/2448196.2448218

M. Olano, D. Baker, W. Griffin, and J. Barczak, Variable Bit Rate GPU Texture Decompression, Computer Graphics Forum, vol.23, issue.3, pp.1299-1308, 2011.
DOI : 10.1109/TIT.1977.1055714

W. Griffin and M. Olano, Evaluating Texture Compression Masking Effects Using Objective Image Quality Assessment Metrics, IEEE Transactions on Visualization and Computer Graphics, vol.21, issue.8, pp.970-979, 2015.
DOI : 10.1109/TVCG.2015.2429576

Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli, and H. , Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

. Seidel, New Measurements Reveal Weaknesses of Image Quality Metrics in Evaluating Graphics Artifacts, ACM Transactions on Graphics, vol.31, issue.147, pp.1-14710, 2012.

G. Lavoué, M. Larabi, and L. Vasa, On the Efficiency of Image Metrics for Evaluating the Visual Quality of 3D Models, IEEE Transactions on Visualization and Computer Graphics, vol.22, issue.8, pp.1987-1999, 2016.
DOI : 10.1109/TVCG.2015.2480079

J. Guo, V. Vidal, I. Cheng, A. Basu, A. Baskurt et al., Subjective and Objective Visual Quality Assessment of Textured 3D Meshes, ACM Transactions on Applied Perception, vol.14, issue.2, pp.1-1120, 2016.
DOI : 10.1109/ICIP.2012.6467150

URL : https://hal.archives-ouvertes.fr/hal-01383623

G. Lavoué and R. Mantiuk, Quality Assessment in Computer Graphics, Visual Signal Quality Assessment: Quality of Experience, pp.243-286, 2015.
DOI : 10.1007/978-3-319-10368-6_9

Z. Wang and A. C. Bovik, Modern Image Quality Assessment, Synthesis Lectures on Image, Video, and Multimedia Processing, vol.17, issue.1, 2006.
DOI : 10.1016/S0923-5965(01)00029-7

J. Lubin, The use of psychophysical data and models in the analysis of display system performance, Digital Images and Human Vision, pp.163-178, 1993.

S. Daly, Visible differences predictor: an algorithm for the assessment of image fidelity, Human Vision, Visual Processing, and Digital Display III, pp.179-206
DOI : 10.1117/12.135952

R. Mantiuk, K. J. Kim, A. G. Rempel, and W. Heidrich, Hdr-vdp-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions, ACM Transactions on Graphics, vol.3014, issue.40, pp.1-40, 2011.

M. Bolin and G. Meyer, A perceptually based adaptive sampling algorithm, Proceedings of the 25th annual conference on Computer graphics and interactive techniques , SIGGRAPH '98, pp.299-309, 1998.
DOI : 10.1145/280814.280924

K. Myszkowski, The Visible Differences Predictor: applications to global illumination problems, Rendering Techniques, pp.223-236, 1998.
DOI : 10.1007/978-3-7091-6453-2_21

M. Ramasubramanian, S. N. Pattanaik, and D. P. Greenberg, A perceptually based physical error metric for realistic image synthesis, Proceedings of the 26th annual conference on Computer graphics and interactive techniques , SIGGRAPH '99, pp.73-82, 1999.
DOI : 10.1145/311535.311543

M. Reddy, SCROOGE:Perceptually-Driven Polygon Reduction, Computer Graphics Forum, vol.15, issue.4, pp.191-203, 1996.
DOI : 10.1111/1467-8659.1540191

D. Luebke and B. Hallen, Perceptually Driven Simplification for Interactive Rendering, Eurographics Workshop on Rendering Techniques, pp.223-234, 2001.
DOI : 10.1007/978-3-7091-6242-2_21

L. Qu and G. Meyer, Perceptually guided polygon reduction, IEEE Transactions on Visualization and Computer Graphics, vol.14, issue.5, pp.1015-1029, 2008.

N. Menzel and M. Guthe, Towards Perceptual Simplification of Models with Arbitrary Materials, Computer Graphics Forum, vol.7, issue.2, pp.2261-2270, 2010.
DOI : 10.1117/12.586757

H. Sheikh and A. Bovik, Image information and visual quality, IEEE Transactions on Image Processing, vol.15, issue.2, pp.430-444, 2006.
DOI : 10.1109/TIP.2005.859378

URL : http://www.znu.ac.ir/data/members/fazli_saeid/DIP/Paper/ISSUE2/01576816.pdf

L. Zhang, X. Mou, and D. Zhang, FSIM: A Feature Similarity Index for Image Quality Assessment, IEEE Transactions on Image Processing, issue.99, pp.2378-2386, 2011.

J. Preiss, F. Fernandes, and P. Urban, Color-Image Quality Assessment: From Prediction to Optimization, IEEE Transactions on Image Processing, vol.23, issue.3, pp.1366-1378, 2014.
DOI : 10.1109/TIP.2014.2302684

Y. Liu and J. Wang, A no-reference metric for evaluating the quality of motion deblurring, SIGGRAPH Asia, 2013.
DOI : 10.1145/2508363.2508391

F. Gao, D. Tao, S. Member, and X. Gao, Learning to Rank for Blind Image Quality Assessment, IEEE Transactions on Neural Networks and Learning Systems, vol.26, issue.10, pp.1-30, 2015.
DOI : 10.1109/TNNLS.2014.2377181

H. Yeganeh and Z. Wang, Objective Quality Assessment of Tone-Mapped Images, IEEE Transactions on Image Processing, vol.22, issue.2, pp.657-667, 2013.
DOI : 10.1109/TIP.2012.2221725

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

A. Brady, J. Lawrence, P. Peers, and W. Weimer, genBRDF, ACM Transactions on Graphics, vol.33, issue.4, pp.1-11, 2014.
DOI : 10.1145/142920.134078

V. Havran, J. Filip, and K. Myszkowski, Perceptually Motivated BRDF Comparison using Single Image Video quality assessment for computer graphics applications, Computer Graphics Forum ACM Transactions on Graphics, vol.35, issue.29 6, pp.1-12, 2010.

R. Herzog, M. Cadik, T. O. Aydin, K. I. Kim, K. Myszkowski et al., NoRM: No-Reference Image Quality Metric for Realistic Image Synthesis, Pacific Graphics, pp.545-554, 2012.
DOI : 10.1145/383745.383748

G. Lavoué, A Multiscale Metric for 3D Mesh Visual Quality Assessment, Computer Graphics Forum, vol.34, issue.1, pp.1427-1437, 2011.
DOI : 10.1016/j.cag.2009.10.004

L. Vá?a and J. Rus, Dihedral Angle Mesh Error: a fast perception correlated distortion measure for fixed connectivity triangle meshes, Computer Graphics Forum, vol.13, issue.4, pp.1715-1724, 2012.
DOI : 10.1109/TIP.2003.819861

M. Corsini, E. D. Gelasca, T. Ebrahimi, and M. Barni, Watermarked 3-D Mesh Quality Assessment, IEEE Transactions on Multimedia, vol.9, issue.2, pp.247-256, 2007.
DOI : 10.1109/TMM.2006.886261

Y. Pan, I. Cheng, and A. Basu, Quality metric for approximating subjective evaluation of 3-D objects, IEEE Transactions on Multimedia, vol.7, issue.2, pp.269-279, 2005.

J. Filip, M. J. Chantler, P. R. Green, M. Haindl, M. Guthe et al., A psychophysically validated metric for bidirectional texture data reduction BTF-CIELab: A Perceptual Difference Measure for Quality Assessment and Compression of BTFs, ACM Transactions on Graphics Computer Graphics Forum, vol.27, issue.28 1, pp.101-113, 2008.

A. Jarabo, H. Wu, J. Dorsey, H. Rushmeier, and D. Gutierrez, Effects of Approximate Filtering on the Appearance of Bidirectional Texture Functions, IEEE Transactions on Visualization and Computer Graphics, vol.20, issue.6, pp.880-892, 2014.
DOI : 10.1109/TVCG.2014.2312016

A. R. Rao and G. L. Lohse, Towards a texture naming system: Identifying relevant dimensions of texture, Proceedings Visualization '93, pp.1649-1669, 1996.
DOI : 10.1109/VISUAL.1993.398872

A. Mali, Available: https://developer.arm.com/products/software-development- tools/graphics-development-tools/mali-texture-compression-tool [44] NVIDIA Mental Ray Available: http://www.nvidia.fr/object/nvidia-mental-ray-fr.html [45] L. Zhang A comprehensive evaluation of full reference image quality assessment algorithms, International Conference on Image Processing (ICIP), pp.1477-1480, 2012.

Z. Wang, E. Simoncelli, and A. Bovik, Multiscale structural similarity for image quality assessment, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, pp.1398-1402, 2003.
DOI : 10.1109/ACSSC.2003.1292216

M. Narwaria, R. K. Mantiuk, M. P. Da, P. L. Silva, and . Callet, HDR-VDP-2.2: a calibrated method for objective quality prediction of high-dynamic range and standard images, Journal of Electronic Imaging, vol.24, issue.1, p.10501, 2015.
DOI : 10.1117/1.JEI.24.1.010501

URL : https://hal.archives-ouvertes.fr/hal-00951337

B. Efron and R. Tibshirani, An Introduction to the Bootstrap, Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 1993.
DOI : 10.1007/978-1-4899-4541-9