Skip to main content

Conclusions and Perspectives

  • Chapter
  • First Online:
Visual Signal Quality Assessment

Abstract

The main contribution of this book is offering an overview of current status, challenges, and new trends of visual quality assessment, from subjective assessment models to objective metrics, covering full-reference (FR), reduced-reference (RR), and no-reference (NR), multiply distorted images, contrast-changed images, mobile media, high dynamic range (HDR) images and videos, medical images, stereoscopic/3D videos, retargeted images and videos, computer graphics and animation quality assessment. Figure 10.1 diagrams the content presented in this book.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. D. Wang, F. Speranza, A. Vincent, T. Martin, and P. Blanchfield, “Toward optimal rate control: a study of the impact of spatial resolution, frame rate, and quantization on subjective video quality and bit rate,” in Visual Communications and Image Processing. International Society for Optics and Photonics, pp. 198–209, 2003.

    Google Scholar 

  2. P. Pérez, M. Jesús, J. R. Jaime, and G. Narciso, “Effect of packet loss in video quality of experience,” Bell Labs Technical Journal. vol. 16, no. 1, pp. 91–104, 2011.

    Article  Google Scholar 

  3. L. Goldmann, D. S. Francesca, D. Frederic, E. Touradj, T. Rudolf, and L. Mauro, “Impact of video transcoding artifacts on the subjective quality,” In Quality of Multimedia Experience (QoMEX), 2010 Second International Workshop on, pp. 52–57. IEEE, 2010.

    Google Scholar 

  4. B. Girod, “What’s wrong with mean-squared error?” In Digital Images and Human Vision, pp. 207–220. MIT press, 1993.

    Google Scholar 

  5. D. M. Chandler, “Seven challenges in image quality assessment: past, present, and future research” ISRN Signal Processing, 2013.

    Google Scholar 

  6. J. Allnatt, “Transmitted-picture assessment” Chichester, UK: Wiley, 1983.

    Google Scholar 

  7. B. Keelan, “Handbook of image quality: characterization and prediction,” CRC Press, 2002.

    Google Scholar 

  8. P. G. Engeldrum, “Psychometric scaling: a toolkit for imaging systems development,” Imcotek Press, 2000.

    Google Scholar 

  9. “Methodology for the subjective assessment of the quality of television pictures,” ITU-R Recommendation BT.500-11, Geneva, 2002.

    Google Scholar 

  10. “Subjective audiovisual quality assessment methods for multimedia applications,” ITU-T Recommendation P.911, Geneva, 1998.

    Google Scholar 

  11. “Subjective methods for the assessment stereoscopic 3DTV systems,” International Telecommunication Union, Geneva, 2012.

    Google Scholar 

  12. B. Keelan, and H. Urabe, “ISO 20462, A psychophysical image quality measurement standard,” Proc. SPIE 5294, pp. 181–189, 2004.

    Article  Google Scholar 

  13. S. Bech, H. Roelof, N. Marco, T. Kees, L. D. J. Henny, H. Paul, and K. P. Sakti, “Rapid perceptual image description (RaPID) method,” In Electronic Imaging: Science and Technology, pp. 317–328. International Society for Optics and Photonics, 1996.

    Google Scholar 

  14. A. B. Watson, “Efficiency of a model human image code,” JOSA A, vol. 4, no. 12, pp. 2401–2417, 1987.

    Article  Google Scholar 

  15. J. A. Redi, and I. Heynderickx, “Image integrity and aesthetics: towards a more encompassing definition of visual quality,” In Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8291, No. 5, p. 35, 2012.

    Google Scholar 

  16. J. A. Solomon, A. B. Watson, and A. Ahumada, “Visibility of DCT basis functions: Effects of contrast masking,” In Data Compression Conference, DCC’94. Proceedings IEEE, pp. 361–370, Mar, 1994.

    Google Scholar 

  17. A. M. Haun, and E. Peli, “Is image quality a function of contrast perception?” In IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, pp. 86510C–86510C, 2013.

    Google Scholar 

  18. P. G. Barten, “Contrast sensitivity of the human eye and its effects on image quality,” Washington: SPIE Optical Engineering Press, vol. 21, 1999.

    Google Scholar 

  19. T. N. Pappas, R. J. Safranek, and J. Chen, “Perceptual criteria for image quality evaluation,” Handbook of image and video processing, pp. 669–684, 2000.

    Google Scholar 

  20. H. Liu, and I. Heynderickx, “A perceptually relevant no-reference blockiness metric based on local image characteristics,” EURASIP Journal on Advances in Signal Processing, 2009.

    Google Scholar 

  21. Z. Wang, and X. Shang, “Spatial pooling strategies for perceptual image quality assessment,” In Image Processing, 2006 IEEE International Conference on, pp. 2945–2948, 2006.

    Google Scholar 

  22. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” Image Processing, IEEE Transactions on, vol. 13, no. 4, pp. 600–612, 2004.

    Article  Google Scholar 

  23. R. Ferzli, and L. J. Karam, “A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB),” Image Processing, IEEE Transactions on, vol. 18, no. 4, pp. 717–728, 2009.

    Article  MathSciNet  Google Scholar 

  24. U. Engelke, H. Kaprykowsky, H. J. Zepernick, and P. Ndjiki-Nya, “Visual attention in quality assessment,” Signal Processing Magazine, IEEE, vol. 28, no. 6, pp. 50–59, 2011.

    Article  Google Scholar 

  25. J. Redi, H. Liu, R. Zunino, ans I. Heynderickx, “Interactions of visual attention and quality perception,” In IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, pp. 78650S–78650S, Feb, 2011.

    Google Scholar 

  26. R. Desimone, and J. Duncan, “Neural mechanisms of selective visual attention,” Annual review of neuroscience, vol. 18, no. 1, pp. 193–222, 1995.

    Article  Google Scholar 

  27. H. Alers, J. Redi, H. Liu, I. Heynderickx, “Studying the effect of optimizing image quality in salient regions at the expense of background content,” J. Electron. Imaging, vol. 22, no. 4, pp. 043012–043012, 2013.

    Article  Google Scholar 

  28. M. Fiedler, T. Hossfeld, and P. Tran-Gia, “A generic quantitative relationship between quality of experience and quality of service,” Network, IEEE, vol. 24, no. 2, pp. 36–41, 2010.

    Article  Google Scholar 

  29. H. J. Kim, D. H. Lee, J. M. Lee, K. H. Lee, W. Lyu, and S. G. Choi, “The QoE evaluation method through the QoS-QoE correlation model,” In Networked Computing and Advanced Information Management, 2008. NCM’08. Fourth International Conference on Network, IEEE, vol. 2, pp. 719–725, 2008.

    Google Scholar 

  30. M. Siller, and J. Woods, “Improving quality of experience for multimedia services by QoS arbitration on a QoE framework,” In Proc. of the 13th Packed Video Workshop, 2003.

    Google Scholar 

  31. G. Ghinea, and J. P. Thomas, “Quality of perception: user quality of service in multimedia presentations,” Multimedia, IEEE Transactions on, vol. 7, no. 4, pp. 786–789, 2005.

    Article  Google Scholar 

  32. H. Ridder, and S. Endrikhovski, “Image quality is FUN: reflections on fidelity, usefulness and naturalness,” In SID Symposium Digest of Technical Papers, Blackwell Publishing Ltd, vol. 33, no. 1, pp. 986–989, May, 2002.

    Google Scholar 

  33. E. Fedorovskaya, C. Neustaedter, and W. Hao, “Image harmony for consumer images,” In Image Processing, 15th IEEE International Conference on, 2008.

    Google Scholar 

  34. P. Kortum, and M. Sullivan, “The effect of content desirability on subjective video quality ratings,” Human factors: the journal of the human factors and ergonomics society, vol. 52, no. 1, pp. 105–118, 2010.

    Article  Google Scholar 

  35. W. A. Mansilla, A. Perkis, ans T. Ebrahimi, “Implicit experiences as a determinant of perceptual quality and aesthetic appreciation,” In Proceedings of the 19th ACM international conference on Multimedia, pp. 153–162, Nov, 2011.

    Google Scholar 

  36. S. Mann and R. Picard, “Being ’Undigitial’ with Digital Cameras: Extending Dynamic Range by Combining Differently Exposed Pictures,” In: Proceedings of IS&T 48th Annual Conference, Society for Imaging Science and Technology, pp. 422–428, 1995.

    Google Scholar 

  37. Spheron, “Spheron HDR VR,” 2008, Available at: http://www.spheron.com/home.html.

  38. G. Ward, “Real Pixels,” Graphic Gems, pp. 15–31, 1991.

    Google Scholar 

  39. G. Ward, “LogLuv Encoding for Full-Gamut High Dynamic Range Images,” Journal of Graphics Tools, vol. 3, no. 1, 1998.

    Google Scholar 

  40. “Industrial Light & Magic,” OpenEXR, 2008, Available at: http://www.openexr.com/.

  41. G. Ward and M. Simmons, “JPEG-HDR: A Backwards-Compatible High Dynamic Range Extension to JPEG,” In: Proceedings of ACM SIGGRAPH 2006 Courses, 2006.

    Google Scholar 

  42. N. Sugiyama, H. Kaida, X. Xue, T. Jinno, N. Adami, and M. Okuda, “HDR Compression Using Optimized Tone Mapping Model,” In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1001–1004, 2009.

    Google Scholar 

  43. R. Mantiuk, A. Efremov, K. Myszkowski, and H. Seidel, “Backward Compatible High Dynamic Range MPEG Video Compression,” ACM Transactions on Graphics, vol. 25, no. 3, pp. 713–723, 2006.

    Article  Google Scholar 

  44. F. Banterle, K. Debattista, A. Artusi, S. Pattanaik, K. Myszkowski, P. Ledda, and A. Chalmers, “High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content,” Computer Graphics Forum, vol. 28, no. 8, 2009.

    Google Scholar 

  45. M. Cadik, M. Wimmer, L. Neumann, and A. Artusi, “Evaluation of HDR tone mapping methods using essential perceptual attributes,” Computers & Graphics, vol. 32, pp. 330–349, 2008.

    Article  Google Scholar 

  46. F. Drago, WL. Martens, K. Myszkowski, and H. Seidel, “Perceptual evaluation of tone mapping operators,” In: Proceedings of the SIGGRAPH 2003 conference on sketches & applications, New York, NY, USA: ACM Press, 2003.

    Google Scholar 

  47. J. Kuang, H. Yamaguchi, C. Liu, G. Johnson, and M. Fairchild, “Evaluating HDR rendering algorithms,” ACM Transactions on Applied Perception, vol. 4, no. 9, 2007.

    Google Scholar 

  48. A. Yoshida, V. Blanz, K. Myszkowski, and H. Seidel, “Perceptual evaluation of tone mapping operators with real-world scenes,” Human Vision & Electronic Imaging X, San Jose, CA, USA: SPIE, pp. 192–203, 2005.

    Google Scholar 

  49. P. Ledda, A. Chalmers, T. Troscianko, and H. Seetzen, “Evaluation of tone mapping operators using a high dynamic range display,” In: Proceedings of the 32nd annual conference on computer graphics and interactive techniques, ACM Press, pp. 640–648, 2005.

    Google Scholar 

  50. M. Ashikhmin, J. Goyal, “A reality check for tone-mapping operators,” ACM Transactions on Applied Perception, vol. 3, no. 4, pp. 399–411, 2006.

    Article  Google Scholar 

  51. G. Eilertsen, R. Wanat, R. Mantiuk, and J. Unger, “Evaluation of tone mapping operators for HDR-video,” In: Computer Graphics Forum Special Issue Proceedings of Pacific Graphics, 2013.

    Google Scholar 

  52. M. Narwaria, M. Silva, P. Callet, and R. Pepion, “Tone mapping Based High Dynamic Range Image Compression: Study of Optimization Criterion and Perceptual Quality,” Optical Engineering (Special Issue on High Dynamic Range Imaging), vol. 52, no. 10, 2013.

    Google Scholar 

  53. M. Narwaria, M. Silva, P. Callet, and R. Pepion, “Impact of Tone Mapping In High Dynamic Range Image Compression,” In: Proc. Eighth International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM), 2014.

    Google Scholar 

  54. R. Mantiuk, K. Jim, A. Rempel, and W. Heidrich, “HDR-VDP-2: a calibrated visual metric for visibility and quality predictions in all luminance conditions,” in ACM Transactions on Graphics (TOG), vol. 30, no. 4, 2011.

    Google Scholar 

  55. D. Tsai, Y. Lee, and E. Matsuyama, “Information entropy measure for evaluation of image quality,” J Digit Imaging, vol. 21, pp. 338–347, 2008.

    Article  Google Scholar 

  56. E. Samei, T. R. Nicole, T. D. James, and C. Ying, “Intercomparison of methods for image quality characterization. I. Modulation transfer functiona,” Medical physics, vol. 33, no. 5, pp. 1454–1465, 2006.

    Article  Google Scholar 

  57. U. Neitzel, G.-K. Susanne, B. Giovanni, and S. Ehsan, “Determination of the detective quantum efficiency of a digital x-ray detector: Comparison of three evaluations using a common image data set,” Medical physics, vol. 31, no. 8, pp. 2205–2211, 2004.

    Article  Google Scholar 

  58. M. Spahn, “Flat detectors and their clinical applications,” Eur Radiol, vol. 15, pp. 1934–1947, 2005.

    Article  Google Scholar 

  59. K. Fettery, and N. Hangiandreou, “Effect of x-ray spectra on the DQE of a computed radiography system,” Med Phys, vol. 28, pp. 241–249, 2001.

    Article  Google Scholar 

  60. T. O. Aydin, R. Mantiuk, K. Myszkowski, and H. P. Seidel, “Dynamic range independent image quality assessment,” ACM Transactions on Graphics (Proc. of SIGGRAPH), vol. 27, no. 3, 2008.

    Google Scholar 

  61. T. O. Aydin, M. Cadik, K. Myszkowski, and H. P. Seidel, “Video quality assessment for computer graphics applications,” ACM Transactions on Graphics (Proc. of SIGGRAPH), vol. 29, no. 6, 2010.

    Google Scholar 

  62. J. Korhonen, C. Mantel, N. Burini, and S. Forchhammer, “Searching for the preferred backlight intensity in liquid crystal displays with local backlight dimming,” In Quality of Multimedia Experience (QoMEX), 2013 Fifth IEEE International Workshop on, July, 2013.

    Google Scholar 

  63. A. Yoshida, V. Blanz, K. Myszkowski, and H. P. Seidel, “Perceptual evaluation of tone mapping operators with real-world scenes,” In Electronic Imaging 2005 International Society for Optics and Photonics, 2005.

    Google Scholar 

  64. I. Wechsung, M. Schulz, K. P. Engelbrecht, J. Niemann, ans S. Moller, “All users are (not) equal-the influence of user characteristics on perceived quality, modality choice and performance,” In Proceedings of the Paralinguistic Information and its Integration in Spoken Dialogue Systems Workshop, Springer New York, Jan, 2011.

    Google Scholar 

  65. J-S Lee, F. D. Simone, and T. Ebrahimi, “Subjective quality evaluation via paired comparison: application to scalable video coding,” IEEE Transactions on Multimedia, vol. 13, no. 5, pp: 882–893, 2011.

    Article  Google Scholar 

  66. C-C Wu, K-T Chen, Y-C Chang, and C-L Lei, “Crowdsourcing multimedia qoe evaluation: A trusted framework,” IEEE transactions on multimedia, vol. 15, no. 5, pp: 1121–1137, 2013.

    Article  Google Scholar 

  67. Q. Xu, Q. Huang, T. Jiang, B. Yan, W. Lin, and Y. Yao, “Hodgerank on random graphs for subjective video quality assessment,” IEEE Transactions on Multimedia, vol. 14, no. 3, pp: 844–857, 2012.

    Article  Google Scholar 

  68. J. Howe, “The rise of crowdsourcing,” Wired magazine, vol. 14, no. 6, pp: 1–4, 2006.

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chenwei Deng .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Deng, C., Wang, S., Ma, L. (2015). Conclusions and Perspectives. 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_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10368-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10367-9

  • Online ISBN: 978-3-319-10368-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics