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Visual Quality Assessment of Stereoscopic Image and Video: Challenges, Advances, and Future Trends

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Visual Signal Quality Assessment

Abstract

Visual quality assessment of stereoscopic/3D images and videos has become an increasingly important and active field of research with the rapid growth in the quantity of stereoscopic/3D content created by the cinema, television, and entertainment industries. However, due to the diversity of stereoscopic/3D display technology and the complexity of human 3D perception, understanding the quality of experience (QoE) of stereoscopic/3D image and video is a difficult and multidisciplinary problem. Objective visual quality assessment attempts to quantify this subjective perception of visual QoE, utilizing tools from engineering, visual science, and psychology. In this chapter, first we discuss the challenges and difficulties one may face while trying to design and develop an effective objective quality assessment (QA) algorithm for stereoscopic images. This discussion is limited to “quality” where the stimulus being perceived is affected by some kind of distortions. In contrast to the success of a variety of objective QA algorithms for 2D images and videos, the field of stereoscopic image and video QA has been less successful in finding widely adopted quality measures. Most objective stereoscopic QA algorithms can be regarded as extensions of 2D QA algorithms, while few of them consider some aspects of depth perception and utilize either computed or measured depth/disparity information from the stereo pairs. We examine and analyze these stereoscopic QA algorithms, while focusing mainly on advances in exploiting natural scene statistics (NSS) and human visual system models in the design of stereoscopic QA algorithms. We also discuss recent work conducted on evaluating visual discomfort and fatigue when viewing stereoscopic images and videos—the more comprehensive “quality-of-experience” evaluation. Finally, we conclude the chapter with a discussion of possible future directions that the field of stereoscopic image and video QA may take. Our summary focuses on gaining a better understanding of depth/disparity sensation, using accurate and robust statistical models of natural stereo pairs, and performing a thorough analysis of various factors affecting the perception of stereoscopic distortions.

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References

  1. American Society for Testing and Materials (ASTM): Standard specification for 3D imaging data exchange. Active Standard ASTM E2807-11 (2013)

    Google Scholar 

  2. Baltes, J., McCann, S., Anderson, J.: Humanoid robots: Abarenbou and daodan. RoboCup-Humanoid League Team Description (2006)

    Google Scholar 

  3. BBC News - Technology: James Cameron: All entertainment ‘inevitably 3D’. http://www.bbc.co.uk/news/entertainment-arts-23790877 (2013)

  4. Benoit, A., Callet, P.L., Campisi, P., Cousseau, R.: Quality assessment of stereoscopic images. EURASIP Journal on Image and Video Processing 2008, 1–13 (2009)

    Article  Google Scholar 

  5. Bensalma, R., Larabi, M.C.: A perceptual metric for stereoscopic image quality assessment based on the binocular energy. Multidimensional Systems and Signal Processing 24(2), 281–316 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  6. Blake, R., Westendorf, D.H., Overton, R.: What is suppressed during binocular rivalry? Perception 9(2), 223–231 (1980)

    Article  Google Scholar 

  7. Boev, A., Gotchev, A., Egiazarian, K., Aksay, A., Akar, G.B.: Towards compound stereo-video quality metric: a specific encoder-based framework. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 218–222 (2006)

    Google Scholar 

  8. Bovik, A.: Automatic prediction of perceptual image and video quality. Proceedings of the IEEE 101(9), 2008–2024 (2013)

    MathSciNet  Google Scholar 

  9. Bovik, A., Chen, D.: Method and apparatus for processing both still and moving visual pattern images. US Patent 5 282 255 (1994)

    Google Scholar 

  10. Bovik, A.C.: The essential guide to video processing. Academic Press (2009)

    Google Scholar 

  11. Brown, M.Z., Burschka, D., Hager, G.D.: Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 993–1008 (2003)

    Article  Google Scholar 

  12. Carnec, M., Le Callet, P., Barba, D.: An image quality assessment method based on perception of structural information. In: Proceedings of the IEEE International Conference on Image Processing, vol. 3, pp. 185–188 (2003)

    Google Scholar 

  13. Chandler, D., Hemami, S.: VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Transactions on Image Processing 16(9), 2284–2298 (2007)

    Article  MathSciNet  Google Scholar 

  14. Chen, M.J., Bovik, A.C., Cormack, L.K.: Study on distortion conspicuity in stereoscopically viewed 3D images. In: Proceedings of the IEEE IVMSP Workshop, pp. 24–29 (2011)

    Google Scholar 

  15. Chen, M.J., Cormack, L.K., Bovik, A.C.: No-reference quality assessment of natural stereopairs. IEEE Transactions on Image Processing 22(9), 3379–3391 (2013)

    Article  MathSciNet  Google Scholar 

  16. Chen, M.J., Cormack, L.K., Bovik, A.C.: Distortion conspicuity on stereoscopically viewed 3D images may correlate to scene content and distortion type. Journal of the Society for Information Display, 21(11) 491–503 (2014)

    Article  Google Scholar 

  17. Chen, M.J., Kwon, D.K., Bovik, A.C.: Study of subject agreement on stereoscopic video quality. In: IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 173–176 (2012)

    Google Scholar 

  18. Chen, M.J., Su, C.C., Kwon, D.K., Cormack, L.K., Bovik, A.C.: Full-reference quality assessment of stereopairs accounting for rivalry. Signal Processing: Image Communication 28(9), 1143–1155 (2013)

    Google Scholar 

  19. Chen, W., Fournier, J., Barkowsky, M., Callet, P.L.: New stereoscopic video shooting rule based on stereoscopic distortion parameters and comfortable viewing zone. In: Proceedings SPIE, Stereoscopic Displays and Applications XXII, vol. 7863 (2011)

    Google Scholar 

  20. Chen, W., Fournier, J., Barkowsky, M., Callet, P.L.: Quality of experience model for 3DTV. In: Proceedings of SPIE, Stereoscopic Displays and Applications XXIII, vol. 8288 (2012)

    Google Scholar 

  21. Craievich, D., Bovik, A.: A stereo VPIC system. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 149–154 (1996)

    Google Scholar 

  22. Cumming, B.G.: An unexpected specialization for horizontal disparity in primate primary visual cortex. Nature 418(6898), 633–636 (2002)

    Article  Google Scholar 

  23. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image restoration by sparse 3D transform-domain collaborative filtering. In: Proceedings of SPIE, Image Processing: Algorithms and Systems VI, vol. 6812 (2008)

    Google Scholar 

  24. Daly, S.J.: Visible differences predictor: an algorithm for the assessment of image fidelity. In: Proceedings of SPIE, Human Vision, Visual Processing, and Digital Display III, vol. 1666, pp. 2–15 (1992)

    Google Scholar 

  25. Daly, S.J., Held, R.T., Hoffman, D.M.: Perceptual issues in stereoscopic signal processing. IEEE Transactions on Broadcasting 57(2), 347–361 (2011)

    Article  Google Scholar 

  26. De Kort, Y.A.W., IJsselsteijn, W.A.: Reality check: the role of realism in stress reduction using media technology. Cyberpsychology & Behavior 9(2), 230–233 (2006)

    Google Scholar 

  27. De Silva, V., Arachchi, H.K., Ekmekcioglu, E., Fernando, A., Dogan, S., Kondoz, A., Savas, S.: Psycho-physical limits of interocular blur suppression and its application to asymmetric stereoscopic video delivery. In: Proceedings of the International Packet Video Workshop, pp. 184–189 (2012)

    Google Scholar 

  28. De Silva, V., Arachchi, H.K., Ekmekcioglu, E., Kondoz, A.: Toward an impairment metric for stereoscopic video: a full-reference video quality metric to assess compressed stereoscopic video. IEEE Transactions on Image Processing 22(9), 3392–3404 (2013)

    Article  MathSciNet  Google Scholar 

  29. DeAngelis, G.C., Ohzawa, I., Freeman, R.D.: Depth is encoded in the visual cortex by a specialized receptive field structure. Nature 352(6331), 156–159 (1991)

    Article  Google Scholar 

  30. Egiazarian, K., Astola, J., Ponomarenko, N., Lukin, V., Battisti, F., Carli, M.: New full-reference quality metrics based on HVS. In: Proceedings of the Second International Workshop on Video Processing and Quality Metrics, vol. 4 (2006)

    Google Scholar 

  31. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. International Journal of Computer Vision 70(1), 41–54 (2006)

    Article  Google Scholar 

  32. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  33. Fleet, D.J., Wagner, H., Heeger, D.J.: Neural encoding of binocular disparity: energy models, position shifts and phase shifts. Vision Research 36(12), 1839–1857 (1996)

    Article  Google Scholar 

  34. Goldmann, L., De Simone, F., Ebrahimi, T.: Impact of acquisition distortions on the quality of stereoscopic images. In: Proceedings of the International Workshop on Video Processing and Quality Metrics for Consumer Electronics (2010)

    Google Scholar 

  35. Goldmann, L., Simone, F.D., Ebrahimi, T.: A comprehensive database and subjective evaluation methodology for quality of experience in stereoscopic video. In: Proceedings of SPIE, Three-Dimensional Image Processing (3DIP) and Applications, vol. 7526 (2010)

    Google Scholar 

  36. Gorley, P., Holliman, N.: Stereoscopic image quality metrics and compression. In: Proceedings of SPIE, Stereoscopic Displays and Applications XIX, vol. 6803 (2008)

    Google Scholar 

  37. Ha, K., Kim, M.: A perceptual quality assessment metric using temporal complexity and disparity information for stereoscopic video. In: Proceedings of the IEEE International Conference on Image Processing, pp. 2525–2528 (2011)

    Google Scholar 

  38. Hewage, C., Martini, M.: Reduced-reference quality metric for 3D depth map transmission. In: Proceedings of the 3DTV-Conference: The True Vision - Capture, Transmission and Display of 3D Video, pp. 1–4 (2010)

    Google Scholar 

  39. Howard, I.P., Rogers, B.J.: Binocular vision and stereopsis. Oxford University Press (1995)

    Google Scholar 

  40. Howard, I.P., Rogers, B.J.: Perceiving in Depth. Oxford University Press (2012)

    Google Scholar 

  41. Huynh-Thu, Q., Le Callet, P., Barkowsky, M.: Video quality assessment: from 2D to 3D – challenges and future trends. In: Proceedings of the IEEE International Conference on Image Processing, pp. 4025–4028 (2010)

    Google Scholar 

  42. International Telecommunication Union (ITU): Subjective video quality assessment methods for multimedia applications. ITU-T Rec. P.910 (2008)

    Google Scholar 

  43. International Telecommunication Union (ITU): Methodology for the subjective assessment of the quality of television pictures. ITU-R Rec. BT.500-11 (2009)

    Google Scholar 

  44. International Telecommunication Union (ITU): Objective perceptual multimedia video quality measurement of HDTV for digital cable television in the presence of a full reference. ITU-T Rec. J.341 (2011)

    Google Scholar 

  45. Jin, L., Boev, A., Gotchev, A., Egiazarian, K.: 3D-DCT based perceptual quality assessment of stereo video. In: Proceedings of the IEEE International Conference on Image Processing, pp. 2521–2524 (2011)

    Google Scholar 

  46. Joint Collaborative Team on Video Coding (JCT-VC) of ITU-T VCEG and ISO/IEC MPEG: High Efficient Video Coding (HEVC). ITU-T Rec. H.265 \(\vert\) ISO/IEC 23008-2 HEVC (2013)

    Google Scholar 

  47. Joint Video Team (JVT) of ITU-T VCEG and ISO/IEC MPEG: Draft ITU-T Recommendation and Final Draft International Standard of Joint Video Specification. ITU-T Rec. H.264 \(\vert\) ISO/IEC 14496-10 AVC (2003)

    Google Scholar 

  48. Julesz, B.: Foundations of Cyclopean Perception. The University of Chicago Press (1971)

    Google Scholar 

  49. Jumisko-Pyykkö, S., Haustola, T., Boev, A., Gotchev, A.: Subjective evaluation of mobile 3D video content: depth range versus compression artifacts. In: Proceedings of SPIE, Multimedia on Mobile Devices 2011 and Multimedia Content Access: Algorithms and Systems V, vol. 7881 (2011)

    Google Scholar 

  50. Kaptein, R.G., Kuijsters, A., Lambooij, M.T.M., IJsselsteijn, W.A., Heynderickx, I.: Performance evaluation of 3D-TV systems. In: Proceedings of SPIE, Image Quality and System Performance V, vol. 6808 (2008)

    Google Scholar 

  51. Kim, D., Sohn, K.: Visual fatigue prediction for stereoscopic image. IEEE Transactions on Circuits and Systems for Video Technology 21(2), 231–236 (2011)

    Article  Google Scholar 

  52. Kolmogorov, V., Zabih, R.: Multi-camera scene reconstruction via graph cuts. In: Proceedings of the 7th European Conference on Computer Vision, vol. 2352, pp. 82–96 (2002)

    Google Scholar 

  53. Lambooij, M., IJsselsteijn, W., Bouwhuis, D.G., Heynderickx, I.: Evaluation of stereoscopic images: beyond 2d quality. IEEE Transactions on Broadcasting 57(2), 432–444 (2011)

    Google Scholar 

  54. Lambooij, M., IJsselsteijn, W., Fortuin, M., Heynderickx, I.: Visual discomfort and visual fatigue of stereoscopic displays: A review. Journal of Imaging Science and Technology 53(3), 1–14 (2009)

    Google Scholar 

  55. Levelt, W.J.M.: On binocular rivalry, vol. 2. Mouton, The Hague (1968)

    Google Scholar 

  56. Liu, Y., Cormack, L.K., Bovik, A.C.: Statistical modeling of 3-D natural scenes with application to bayesian stereopsis. IEEE Transactions on Image Processing 20(9), 2515–2530 (2011)

    Article  MathSciNet  Google Scholar 

  57. López, J.P., Rodrigo, J.A., Jiménez, D., Menéndez, J.M.: Stereoscopic 3D video quality assessment based on depth maps and video motion. EURASIP Journal on Image and Video Processing 2013(1), 1–14 (2013)

    Article  Google Scholar 

  58. Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)

    Google Scholar 

  59. Maalouf, A., Larabi, M.C.: CYCLOP: a stereo color image quality assessment metric. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1161–1164 (2011)

    Google Scholar 

  60. Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial & Applied Mathematics 11(2), 431–441 (1963)

    Article  MATH  MathSciNet  Google Scholar 

  61. Meegan, D.V., Stelmach, L.B., Tam, W.J.: Unequal weighting of monocular inputs in binocular combination: Implications for the compression of stereoscopic imagery. Journal of Experimental Psychology: Applied 7(2), 143–153 (2001)

    Google Scholar 

  62. Meesters, L.M.J., IJsselsteijn, W.A., Seuntiens, P.J.H.: A survey of perceptual evaluations and requirements of three-dimensional TV. IEEE Transactions on Circuits and Systems for Video Technology 14(3), 381–391 (2004)

    Google Scholar 

  63. Menz, M.D., Freeman, R.D.: Stereoscopic depth processing in the visual cortex: a coarse-to-fine mechanism. Nature Neuroscience 6(1), 59–65 (2002)

    Article  Google Scholar 

  64. Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12), 4695–4708 (2012)

    Article  MathSciNet  Google Scholar 

  65. Mittal, A., Moorthy, A.K., Ghosh, J., Bovik, A.C.: Algorithmic assessment of 3D quality of experience for images and videos. In: Proceedings of the IEEE Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop, pp. 338–343 (2011)

    Google Scholar 

  66. Mittal, A., Soundararajan, R., Bovik, A.: Making a “completely blind” image quality analyzer. IEEE Signal Processing Letters 20(3), 209–212 (2013)

    Article  Google Scholar 

  67. Moorthy, A., Seshadrinathan, K., Soundararajan, R., Bovik, A.: Wireless video quality assessment: A study of subjective scores and objective algorithms. IEEE Transactions on Circuits and Systems for Video Technology 20(4), 587–599 (2010)

    Article  Google Scholar 

  68. Moorthy, A., Su, C.C., Chen, M.J., Mittal, A., Cormack, L.K., Bovik, A.C.: LIVE 3D Image Quality Database Phase I and Phase II. http://live.ece.utexas.edu/research/quality/live_3dimage.html

  69. Moorthy, A.K., Bovik, A.C.: Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Transactions on Image Processing 20(12), 3350–3364 (2011)

    Article  MathSciNet  Google Scholar 

  70. Moorthy, A.K., Bovik, A.C.: Visual quality assessment algorithms: What does the future hold? Multimedia Tools and Applications 51(2), 675–696 (2011)

    Article  Google Scholar 

  71. Moorthy, A.K., Bovik, A.C.: A survey on 3D quality of experience and 3D quality assessment. In: Proceedings of SPIE, Human Vision and Electronic Imaging XVIII, vol. 8651 (2013)

    Google Scholar 

  72. Moorthy, A.K., Su, C.C., Mittal, A., Bovik, A.C.: Subjective evaluation of stereoscopic image quality. Signal Processing: Image Communication 28(8), 870–883 (2013)

    Google Scholar 

  73. Motion Picture Association of America (MPAA): Theatrical market statistics. http://www.mpaa.org/policy/industry (2012)

  74. Ohzawa, I., Freeman, R.D.: The binocular organization of complex cells in the cat’s visual cortex. Journal of Neurophysiology 56(1), 243–259 (1986)

    Google Scholar 

  75. Ohzawa, I., Freeman, R.D.: The binocular organization of simple cells in the cat’s visual cortex. Journal of Neurophysiology 56(1), 221–242 (1986)

    Google Scholar 

  76. Okada, Y., Ukai, K., Wolffsohn, J.S., Gilmartin, B., Iijima, A., Bando, T.: Target spatial frequency determines the response to conflicting defocus- and convergence-driven accommodative stimuli. Vision Research 46(4), 475–484 (2006)

    Article  Google Scholar 

  77. Olshausen, B., Field, D.: Natural image statistics and efficient coding. Network: Computation in Nerual Systems 7(2), 333–339 (1996)

    Article  Google Scholar 

  78. Olshausen, B.A., Field, D.J.: Vision and the coding of natural images. American Scientist 88, 238–245 (2000)

    Article  Google Scholar 

  79. Park, J., Lee, S., Bovik, A.C.: 3D visual discomfort prediction: vergence, foveation, and the physiological optics of accommodation. IEEE Journal of Selected Topics in Signal Processing, 8(3), 415–427 (2014)

    Article  Google Scholar 

  80. Park, J., Oh, H., Lee, S.: IEEE Standards Association Stereo Image Database. http://grouper.ieee.org/groups/3dhf/

  81. Pinson, M.H., Wolf, S.: A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting 50(3), 312–322 (2004)

    Article  Google Scholar 

  82. Ponomarenko, N., Silvestri, F., Egiazarian, K., Carli, M., Astola, J., Lukin, V.: On between-coefficient contrast masking of DCT basis functions. In: Proceedings of the Third International Workshop on Video Processing and Quality Metrics, vol. 4 (2007)

    Google Scholar 

  83. Potetz, B., Lee, T.S.: Statistical correlations between two-dimensional images and three-dimensional structures in natural scenes. Journal of the Optical Society of America A 20(7), 1292–1303 (2003)

    Article  Google Scholar 

  84. Puri, A., Kollarits, R.V., Haskell, B.G.: Basics of stereoscopic video, new compression results with MPEG-2 and a proposal for MPEG-4. Signal Processing: Image Communication 10(1), 201–234 (1997)

    Google Scholar 

  85. Read, J.: Early computational processing in binocular vision and depth perception. Progress in Biophysics and Molecular Biology 87(1), 77–108 (2005)

    Article  Google Scholar 

  86. Rosenholtz, R., Watson, A.B.: Perceptual adaptive JPEG coding. In: Proceedings of the IEEE International Conference on Image Processing, vol. 1, pp. 901–904 (1996)

    Google Scholar 

  87. Ruderman, D.L.: The statistics of natural images. Network: Computation in Neural Systems 5(4), 517–548 (1994)

    Article  MATH  Google Scholar 

  88. Ryu, S., Kim, D.H., Sohn, K.: Stereoscopic image quality metric based on binocular perception model. In: Proceedings of the IEEE International Conference on Image Processing, pp. 609–612 (2012)

    Google Scholar 

  89. Saad, M., Bovik, A., Charrier, C.: Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing 21(8), 3339–3352 (2012)

    Article  MathSciNet  Google Scholar 

  90. Sazzad, Z., Akhter, R., Baltes, J., Horita, Y.: Objective no-reference stereoscopic image quality prediction based on 2D image features and relative disparity. Advances in Multimedia 2012(8), 1–16 (2012)

    Article  Google Scholar 

  91. Sazzad, Z.P., Yamanaka, S., Kawayokeita, Y., Horita, Y.: Stereoscopic image quality prediction. In: Proceedings of the International Workshop on Quality of Multimedia Experience, pp. 180–185 (2009)

    Google Scholar 

  92. Scharstein, D.: Middlebury stereo datasets. http://vision.middlebury.edu/stereo/data/

  93. Seshadrinathan, K., Soundararajan, R., Bovik, A., Cormack, L.: Study of subjective and objective quality assessment of video. IEEE Transactions on Image Processing 19(6), 1427–1441 (2010)

    Article  MathSciNet  Google Scholar 

  94. Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: LIVE Video Quality Database. http://live.ece.utexas.edu/research/quality/live_video.html

  95. Seuntiens, P., Meesters, L., Ijsselsteijn, W.: Perceived quality of compressed stereoscopic images: effects of symmetric and asymmetric JPEG coding and camera separation. ACM Transactions on Applied Perception 3(2), 95–109 (2006)

    Article  Google Scholar 

  96. Sheikh, H., Bovik, A.: Image information and visual quality. IEEE Transactions on Image Processing 15(2), 430–444 (2006)

    Article  Google Scholar 

  97. Sheikh, H.R., Sabir, M.F., Bovik, A.C.: A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing 15(11), 3440–3451 (2006)

    Article  Google Scholar 

  98. Sheikh, H.R., Wang, Z., Cormack, L.K., Bovik, A.C.: LIVE Image Quality Assessment Database. http://live.ece.utexas.edu/research/quality/subjective.htm

  99. Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Annual Review of Neuroscience 24(1), 1193–1216 (2001)

    Article  Google Scholar 

  100. Soundararajan, R., Bovik, A.: RRED indices: Reduced reference entropic differencing for image quality assessment. IEEE Transactions on Image Processing 21(2), 517–526 (2012)

    Article  MathSciNet  Google Scholar 

  101. Su, C.C., Cormack, L.K., Bovik, A.C.: Color and depth priors in natural images. IEEE Transactions on Image Processing 22(6), 2259 – 2274 (2013)

    Article  MathSciNet  Google Scholar 

  102. Su, C.C., Cormack, L.K., Bovik, A.C.: Bivariate statistical modeling of color and range in natural scenes. In: Proceedings of SPIE, Human Vision and Electronic Imaging XIX, vol. 9014 (2014)

    Google Scholar 

  103. Tam, W.J., Speranza, F., Yano, S., Shimono, K., Ono, H.: Stereoscopic 3D-TV: Visual comfort. IEEE Transactions on Broadcasting 57(2), 335–346 (2011)

    Article  Google Scholar 

  104. Tang, H., Joshi, N., Kapoor, A.: Learning a blind measure of perceptual image quality. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 305–312 (2011)

    Google Scholar 

  105. Tovée, M.J.: An introduction to the visual system. Cambridge University Press (1996)

    Google Scholar 

  106. Urvoy, M., Barkowsky, M., Cousseau, R., Koudota, Y., Ricorde, V., Le Callet, P., Gutiérrez, J., García, N.: NAMA3DS1-COSPAD1: Subjective video quality assessment database on coding conditions introducing freely available high quality 3D stereoscopic sequences. In: Proceedings of the International Workshop on Quality of Multimedia Experience, pp. 109–114 (2012)

    Google Scholar 

  107. Wang, X., Yu, M., Yang, Y., Jiang, G.: Research on subjective stereoscopic image quality assessment. In: Proceedings of SPIE, Multimedia Content Access: Algorithms and Systems III, vol. 7255 (2009)

    Google Scholar 

  108. Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Processing Letters 9(3), 81–84 (2002)

    Article  Google Scholar 

  109. Wang, Z., Bovik, A.C.: Modern image quality assessment. Synthesis Lectures on Image, Video, and Multimedia Processing 2(1), 1–156 (2006)

    Article  Google Scholar 

  110. Wang, Z., Bovik, A.C.: Mean squared error: love it or leave it? a new look at signal fidelity measures. IEEE Signal Processing Magazine 26(1), 98–117 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  112. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2003, vol. 2, pp. 1398–1402 (2003)

    Google Scholar 

  113. Western Ophthalmics Corporation: Stereo Randot Test. http://www.west-op.com/stereorandot.html

  114. Westin, C.F.: Extracting brain connectivity from diffusion MRI [life sciences]. IEEE Signal Processing Magazine 24(6), 124–152 (2007)

    Article  Google Scholar 

  115. William, A.M., Bailey, D.L.: Stereoscopic visualization of scientific and medical content. In: ACM SIGGRAPH 2006 Educators Program, 26 (2006)

    Google Scholar 

  116. Winkler, S.: Image and video quality resources. http://stefan.winkler.net/resources.html

  117. Winkler, S.: Analysis of public image and video databases for quality assessment. IEEE Journal of Selected Topics in Signal Processing 6(6), 616–625 (2012)

    Article  Google Scholar 

  118. Yasakethu, S.L.P., Hewage, C.T.E.R., Fernando, W., Kondoz, A.: Quality analysis for 3D video using 2D video quality models. IEEE Transactions on Consumer Electronics 54(4), 1969–1976 (2008)

    Article  Google Scholar 

  119. Ye, P., Doermann, D.: No-reference image quality assessment using visual codebooks. IEEE Transactions on Image Processing 21(7), 3129–3138 (2012)

    Article  MathSciNet  Google Scholar 

  120. You, J., Xing, L., Perkis, A., Wang, X.: Perceptual quality assessment for stereoscopic images based on 2D image quality metrics and disparity analysis. In: Proceedings of the International Workshop on Video Processing and Quality Metrics (2010)

    Google Scholar 

  121. Zwicker, M., Yea, S., Vetro, A., Forlines, C., Matusik, W., Pfister, H.: Display pre-filtering for multi-view video compression. In: Proceedings of the 15th International Conference on Multimedia, pp. 1046–1053 (2007)

    Google Scholar 

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Authors and Affiliations

  1. The University of Texas at Austin, Austin, TX, USA

    Che-Chun Su & Alan Conrad Bovik

  2. Qualcomm Inc., San Diego, CA, USA

    Anush Krishna Moorthy

Authors
  1. Che-Chun Su
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  2. Anush Krishna Moorthy
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  3. Alan Conrad Bovik
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Correspondence to Che-Chun Su .

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Editors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 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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© 2015 Springer International Publishing Switzerland

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Su, CC., Moorthy, A.K., Bovik, A.C. (2015). Visual Quality Assessment of Stereoscopic Image and Video: Challenges, Advances, and Future Trends. 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_7

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  • DOI: https://doi.org/10.1007/978-3-319-10368-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10367-9

  • Online ISBN: 978-3-319-10368-6

  • eBook Packages: EngineeringEngineering (R0)

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