Skip to main content
SpringerLink
Log in

Introduction: State of the Play and Challenges of Visual Quality Assessment

  • Chapter
  • First Online:
Visual Signal Quality Assessment

Abstract

Quality of visual signals perceived by human observers has always been a critical issue, so has been the measurement of the signal quality throughout a process chain of acquisition/reproduction, encoding, transmission or storage, decoding, and visualization/display associated with a designated application or service in either analogue or digital form. Digital visual signals compressed using various coding techniques exhibit coding distortions which differ from those known to be associated with analogue visual signals and, therefore, require provision of both subjective and objective distortion or quality measures which quantitatively assess and evaluate the visual picture quality for the purposes of system or service evaluation and optimization. A number of fundamental issues are examined to put the current discussions and activities into perspective and context, including relationship between picture quality assessment and coding designs, how to measure effectiveness of visual signal compression performance, different scales used for visual quality assessment and their intended applications, picture distortion or quality ratings for rate-perceptual-distortion (RpD) optimization.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
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

Notes

  1. 1.

    Visual signals or pictures refer to images, video, image sequences or motion pictures [118].

  2. 2.

    In [24], 10 MHz sampling rate was used for PCM coding of a 5 MHz analogue TV signal with 8 bits per sample, compared with 8 kHz sampling rate and 8 bits per sample for voice using telephone at the time.

  3. 3.

    Digital storage media commonly used by digital video cameras currently include memory stick, memory card, and flash memory.

  4. 4.

    For example, Australia switched to digital-only TV broadcasting on 10 December 2013 as per Australian Government announcement via “Australia’s Ready for Digital TV.”

  5. 5.

    QoE as defined by International Telecommunication Union Study Group (ITU SG) 12 is application or service specific and influenced by user expectations and context [35], and therefore necessitates assessments of perceived service quality and/or utility (or usefulness) of the service [76].

  6. 6.

    QoS as defined by ITU SG 12 is the totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service [36], e.g., error rates, bandwidth, throughput, transmission delay, availability, jitter, and so on [91].

  7. 7.

    Perceptually lossless coded visual signals incur no discernable visual difference compared with their originals while they may have undergone irreversible information loss [106, 118].

  8. 8.

    Perceptual entropy defines the theoretical lower bound of perceptually lossless visual signal coding in a similar way that entropy does the lower bound of information lossless coding [81].

  9. 9.

    Prior knowledge plays an important part in subjective rating exercise using ACR which forms a benchmark experience or a point of reference in what constitutes the “best” or “excellent” picture quality as they have seen or experienced, and is also exemplified by the entropy masking effect which is imposed solely by an observer’s unfamiliarity with the masker [110].

  10. 10.

    ŝ consists of three distortion measures, including blur-ringing and false edges, localized jerky motion due to frame repetition, and temporal distortion due to periodic noise, uncorrected block errors due to transmission errors or packet loss and maximum jerky motion of the time history [111].

  11. 11.

    Institute for Telecommunication Sciences, National Telecommunications & Information Administration (NTIA), USA.

  12. 12.

    American National Standards Institute.

  13. 13.

    International Telecommunication Union, Telecommunication Standardization Sector.

  14. 14.

    In [34, 72], these seven indicators/measures or sub-metrics were referred to as parameters. Weighting constants for the seven measures are referred to parameters or coefficients here which are determined or optimized for the outputs of VQM to fit the subjective test data.

  15. 15.

    In [84], it is referred to as “HVS distortion visual noise.”

  16. 16.

    The GSM model in wavelet domain is an RF expressed as a product of two independent RFs and is used to approximate key statistical features of natural pictures [98].

References

  1. Ahumada AJ (1992) Luminance-model-based DCT quantization for color image compression. In: Proc SPIE 1666:365–374

    Google Scholar 

  2. Antonini M, Barlaud M, Mathieu P et al (1992) Image coding using wavelet transform. IEEE Trans Image Process 1(2):205–220

    Google Scholar 

  3. Berger T, Gibson JD (1998) Lossy source coding. IEEE Trans Inf Theory 44(10):2693–2723

    MATH  MathSciNet  Google Scholar 

  4. Bovik AC (2013) Automatic prediction of perceptual image and video quality. Proc IEEE 101(9):2008–2024

    MathSciNet  Google Scholar 

  5. Budrikis ZL (1972) Visual fidelity criterion and modeling. Proc IEEE 60(7):771–779

    Google Scholar 

  6. Carnec M, Le Callet P, Barba D (2008) Objective quality assessment of color images based on a generic perceptual reduced reference. Signal Process Image Commun 23(4):239–256

    Google Scholar 

  7. Chandler DM (2013) Seven challenges in image quality assessment: Past, present, and future research. ISRN Signal Processing Article ID 905685

    Google Scholar 

  8. Chandler DM, Hemami SS (2005) Dynamic contrast-based quantization for lossy wavelet image compression. IEEE Trans Image Process 14(4):397–410

    Google Scholar 

  9. Chandler DM, Hemami SS (2007) VSNR: A wavelet-based visual signal-to-noise ratio for natural images. IEEE Trans Image Process 16(9):2284–2298

    MathSciNet  Google Scholar 

  10. Chikkerur S, Sundaram V, Reisslein M et al (2011) Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison. IEEE Trans Broadcasting 57(2):165–182

    Google Scholar 

  11. Chou C-H, Chen C-W (1996) A perceptually optimized 3-D subband image codec for video communication over wireless channels. IEEE Trans Circuits Syst Video Technol 6(4):143–156

    MathSciNet  Google Scholar 

  12. Chou C-H, Li Y-C (1995) A perceptually tuned subband image coder based on the measure of just-noticeable distortion profile. IEEE Trans Circuits Syst Video Technol 5(6):467–476

    Google Scholar 

  13. Chou C, Liu K (2010) A perceptually tuned watermarking scheme for color images. IEEE Trans Image Process 19(11):2966–2982

    MathSciNet  Google Scholar 

  14. Clarke RJ (1985) Transform Coding of Images. Academic Presss, New York, NY

    Google Scholar 

  15. Corriveau P (2006) Video Quality Testing. In: Wu HR, Rao KR (eds) Digital video image quality and perceptual coding. CRC Press, Boca Raton, FL. 125–153

    Google Scholar 

  16. Corriveau P, Gojmerac C, Hughes B. et al (1999) All subjective scales are not created equal: The effect of context on different scales. Signal Processing 77(1):1–9

    MATH  Google Scholar 

  17. Coverdale P, Möller S, Raake A et al (2011) Multimedia quality assessment standards in ITU-T SG12. IEEE Signal Process Mag 28(6):91–97

    Google Scholar 

  18. Cutler CC (1952) Differential quantization of communication signals. U.S. Patent 2 605 361

    Google Scholar 

  19. Daly S (1993) The visible differences predictor: An algorithm for the assessment of image fidelity. In: Watson AB (ed) Digital Images and Human Vision. MIT Press, Cambridge, MA. 179–206.

    Google Scholar 

  20. Daly SJ, Held RT, Hoffman DM (2011) Perceptual issues in stereoscopic signal processing. IEEE Trans Broadcast 57(2):347–361

    Google Scholar 

  21. Egiazarian K, Astola J, Ponomarenko N, et al (2006) New full-reference quality metrics based on HVS. In: Proc VPQM-06. Paper 9

    Google Scholar 

  22. Foley JM (1994) Human luminance pattern-vision mechanisms: Masking experiments require a new model. J Opt Soc Amer A 11(6):1710–1719

    Google Scholar 

  23. Girod B (1993) What’s wrong with mean-squared error. In: Watson AB (ed) Digital images and human vision. MIT Press, Cambridge, MA. 207–220.

    Google Scholar 

  24. Goodall WM (1951) Television by pulse code modulation. Bell Syst Tech J 28:33–49

    Google Scholar 

  25. Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  26. Green PE Jr (1993) Fiber optic networks. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  27. Harrison CW (1952) Experiments with Linear Prediction in Television. Bell Sys Techn J 31(4):764–783

    Google Scholar 

  28. Hassan M, Bhagvati C (2012) Structural similarity measure for color images. Int J Comput Appl (0975 – 8887) 43(14):7–12

    Google Scholar 

  29. Hemami SS, Reibman AR (2010) No-reference image and video quality estimation: Applications and human-motivated design. Signal Process Image Commun 25:469–481

    Google Scholar 

  30. Höntsch I, Karam LJ (2000) Locally adaptive perceptual image coding. IEEE Trans Image Process 9(9):1285–1483

    Google Scholar 

  31. Inglis AF, Luther AC (1993) Video engineering, 2nd edn. McGraw-Hill, New York

    Google Scholar 

  32. ITU-R (2004) Objective perceptual video quality measurement techniques for standard definition digital broadcast television in the presence of a full reference. Rec. BT.1683

    Google Scholar 

  33. ITU-R (2012) Methodology for the subjective assessment of the quality of television pictures. Rec. BT.500-13

    Google Scholar 

  34. ITU-T (2004) Objective perceptual video quality measurement techniques for digital cable television in the presence of a full reference, Rec. J.144

    Google Scholar 

  35. ITU-T (2008) Vocabulary for performance and quality of service, Amendment 2: New definitions for inclusion in Recommendation ITU-T P.10/G.100. Rec. P.10/G.100

    Google Scholar 

  36. ITU-T (2011) Vocabulary for performance and quality of service, Amendment 3: New definitions for inclusion in Recommendation ITU-T P.10/G.100. Rec. P.10/G.100

    Google Scholar 

  37. ITU-T (2008) Subjective video quality assessment methods for multimedia applications. Rec. P.910

    Google Scholar 

  38. Jayant NS, Johnston J, Safranek and R (1993) Signal compression based on models of human perception. Proc IEEE 81(10):1385–1422

    Google Scholar 

  39. Jayant NS, Noll P (1984) Digital coding of waveforms: Principles and applications to speech and video. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  40. Johnson RA, Bhattacharyya GK (1992) Statistics: Principles and methods, 2nd edn. John Wiley & Sons, New York

    MATH  Google Scholar 

  41. Karam LJ, Ebrahimi T, Hemami S et al (eds) (2009) Special issue on visual media quality assessment. IEEE J Sel Topics Signal Process 3(2)

    Google Scholar 

  42. Kubota A, Smolic A, Magnor M et al (2007) Multiview imaging and 3DTV-Special issue overview and introduction. IEEE Signal Process Mag 24(6):10–21

    Google Scholar 

  43. Jia Y, Lin W, Kassim AA (2006) Estimating just-noticeable distortion for video. IEEE Trans Circuits Syst Video Technol 16(7):820–829

    Google Scholar 

  44. Van den Branden Lambrecht CJ (ed) (1998) Special issue on image and video quality metrics. Signal Process 70(3)

    Google Scholar 

  45. Larson EC, Chandler DM (2010) Most apparent distortion: Full-reference image quality assessment and the role of strategy. J Electron Imaging 19(1):ID 011006.

    Google Scholar 

  46. Limb JO (1967) Source-receiver encoding of television signals. Proc. IEEE 55(3): 364–379

    Google Scholar 

  47. Lin W. (2006) Computational models for just-noticeable difference. In: Wu HR, Rao KR (eds) Digital video image quality and perceptual coding, CRC Press, Boca Raton, FL 281–303

    Google Scholar 

  48. Lin W, Ebrahimi T, Loizou PC et al (eds) (2012) Special issue on new subjective and objective methodologies for audio and visual signal processing. IEEE J Sel Top Signal Process 6

    Google Scholar 

  49. Lin W, Kuo C-CJ (2011) Perceptual visual quality metrics: A survey. J Vis Commun Image R 22:297–312

    Google Scholar 

  50. Liu Z, Karam LJ, Watson AB (2006) JPEG2000 encoding with perceptual distortion control. IEEE Trans Image Process 15(7):1763–1778

    Google Scholar 

  51. Liu A, Lin W, Paul M et al (2010) Just noticeable difference for image with decomposition model for separating edge and textured regions. IEEE Trans Circuits Syst Video Technol 20(11):1648–1652

    Google Scholar 

  52. Lubin J (1993) The use of psychophysical data and models in the analysis of display system performance. In: Watson AB (ed) Digital Images and Human Vision. MIT Press, Cambridge, MA. 163–178.

    Google Scholar 

  53. Luigi A, Chen CW, Tasos D (eds) (2012) QoE Management in Emerging Multimedia Services. IEEE Communications Magazine 50(4):18–19

    Google Scholar 

  54. Luo MR, Cui G, Rigg B (2001) The development of the CIE 2000 colour-difference formula: CIEDE2000. Col Res App 26(5):340–350

    Google Scholar 

  55. Mannos JL, Sakrison DJ (1974) The effects of a visual fidelity criterion on the encoding of images. IEEE Trans Inf Theory IT-20(4):525–536

    Google Scholar 

  56. Miyahara M. (1988) Quality assessments for visual service. IEEE Commun Mag 26:51–60

    Google Scholar 

  57. Miyahara M, Kawada R (2006) Philosophy of picture quality scale. In: Wu HR, Rao KR (eds) Digital video image quality and perceptual coding. CRC Press, Boca Raton, FL. 181–223

    Google Scholar 

  58. Miyahara M, Kotani K, Algazi VR (1998) Objective picture quality scale (PQS) for image coding. IEEE Trans Commun 46(9):1215–1226

    Google Scholar 

  59. Muntean G-M, Ghinea G, Frossard P et al (eds) (2008) Special Issue: Quality Issues on Multimedia Broadcasting. IEEE Trans Broadcast 54(3), Pt.II

    Google Scholar 

  60. Naser K, Ricordel V, Le Callet P (2014) Experimenting texture similarity metric STSIM for intra prediction mode selection and block partitioning in HEVC. In: Proc DSP2014: 882–887

    Google Scholar 

  61. National Institute of Standards and Technology (2000) Final report from the video quality experts group on the validation of objective models of video quality assessment. Available [Online] via: ftp.its.bldrdoc.gov

  62. Oh H, Bilgin A, Marcellin MW (2013) Visually lossless encoding for JPEG2000. IEEE Trans Image Process 22(1):189–201

    MathSciNet  Google Scholar 

  63. Ohm J-R, Sullivan GJ, Schwarz H et al (2012) Comparison of the coding efficiency of video coding standards-including high efficiency video coding (HEVC). IEEE Trans Circuits Syst Video Technol 22(12):1669–1684

    Google Scholar 

  64. O’Neal JB Jr (1966) Predictive quantizing aystems (differential pulse code modulation) for the transmission of television signals. Bell Sys Techn J 45(5):689–721

    Google Scholar 

  65. Onural L (2007) Television in 3-D: What are the prospects? Proc IEEE 95(6):1143–1145

    Google Scholar 

  66. OpenJPEG (2014) Windows Binaries of OpenJPEG library and codecs Labels: OpSys-Windows Type-Executable, (Version 2.0). Available: http://www.openjpeg.org/

  67. Pappas TN, Neuhoff DL, de Ridder H et al (2013) Image analysis: Focus on texture similarity,” Proc IEEE 101(9):2044–2057

    Google Scholar 

  68. Pica A, Isnardi M, Lubin J (2006) HVS based perceptual video encoders. In: Wu HR, Rao KR (eds) Digital video image quality and perceptual coding. CRC Press, Boca Raton, FL. 337–360

    Google Scholar 

  69. Párraga CA, Troscianko T, Tolhurst DJ (2005) The effects of amplitude-spectrum statistics on foveal and peripheral discrimination of changes in natural images, and a multi-resolution model. Vis Res 45(25–26):3145–3168

    Google Scholar 

  70. Peterson HA, Ahumada AJ, Watson AB (1993) Improved detection model for DCT coefficient quantization. In: Proc SPIE 1913:191–201

    Google Scholar 

  71. Pinson MH, Ingram W, Webster A (2011) Audiovisual quality components. IEEE Signal Process Mag 28(6):60–67

    Google Scholar 

  72. Pinson MH, Wolf S (2004) A new standardized method for objectively measuring video quality. IEEE Trans Broadcast 50(3):312–322

    Google Scholar 

  73. Ponomarenko N, Silvestri F, Egiazarian K et al (2007) On Between-Coefficient Contrast Masking of DCT Basis Functions. In: Proc. VPQM-07. Paper 11

    Google Scholar 

  74. Quan H-T, Le Callet P (2010) Video quality assessment: From 2D to 3D-challenges and future trends. In: Proc IEEE ICIP2010 4025–4028

    Google Scholar 

  75. Ramos MG, Hemami SS (2001) Suprathreshold wavelet coefficient quantization in complex stimuli: psychophysical evaluation and analysis. J Opt Soc Am A 18(10):2385–2397

    Google Scholar 

  76. Rouse DM, Hemami SS, Pépion R et al (2011) Estimating the usefulness of distorted natural images using an image contour degradation measure. J Opt Soc Am A 28(2):157–188

    Google Scholar 

  77. Safranek RJ (1994) A JPEG compliant encoder utilizing perceptually based quantization. In: Proc SPIE 2179:117–126

    Google Scholar 

  78. Safranek RJ, Johnston JD (1989) A perceptually tuned subband image coder with image dependent quantization and post-quantization. In: Proc IEEE ICASSP 1945–1948

    Google Scholar 

  79. Sampat MP, Wang Z, Gupta S et al (2009) Complex wavelet structural similarity: A new image similarity index. IEEE Trans Image Process 18(11):2385–2401

    MathSciNet  Google Scholar 

  80. Sekuler R, Blake R (1994) Perception, 3rd ed. McGraw-Hill, New York, NY

    Google Scholar 

  81. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423 and 623–656

    Google Scholar 

  82. Shannon CE (1949) Communication in the presence of noise. Proc IRE 37(1):10–21

    MathSciNet  Google Scholar 

  83. Shannon CE (1959) Coding theorems for a discrete source with a fidelity criteria. In: IRE Nat. Conv. Record. 7:142–163.

    Google Scholar 

  84. Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444

    Google Scholar 

  85. Sheikh HR, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15(11):3440–3451

    Google Scholar 

  86. Simoncelli EP, Freeman WT, Adelson EH et al (1992) Shiftable multiscale transforms. IEEE Trans Inform Theory 38(2):587–607

    MathSciNet  Google Scholar 

  87. Strela V, Portilla J, Simoncelli E (2000) Image denoising using a local Gaussian scale mixture model in the wavelet domain. Proc SPIE 4119:363–371

    Google Scholar 

  88. Tan DM, Tan C-S, Wu HR (2010) Perceptual colour image coder with JPEG2000. IEEE Trans Image Process 19(2):374–383

    MathSciNet  Google Scholar 

  89. Tan DM, Wu D (2013) Perceptually lossless and perceptually enhanced image compression system & method, Patent Appl No:WO2013/063638 A2. WIPO, Geneva, Switzerland

    Google Scholar 

  90. Tan DM, Wu HR, Yu Z (2004) Perceptual coding of digital monochrome images. IEEE Signal Process Lett 11(2):239–242

    Google Scholar 

  91. Tanenbaum AS (2003) Computer networks, 4th ed. Pearson Education Inc., Upper Saddle River, NJ

    Google Scholar 

  92. Teo PT, Heeger DJ (1994) Perceptual image distortion. In: Proc IEEE ICIP 1994 2:982–986

    Google Scholar 

  93. van den Branden Lambrecht CJ (1996) Perceptual models and architectures for video coding applications. Ph.D. dissertation, Swiss Federal Inst Technol, Zurich, Switzerland

    Google Scholar 

  94. Vetro A, Tourapis AM, Müller K et al (2011) 3D-TV content storage and transmission. IEEE Trans Broadcast 57(2):384–394

    Google Scholar 

  95. Vetterli M, Kovačević J (1995) Wavelets and subband coding. Prentice-Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  96. Visual Information Systems Research Group (1995) A methodology for imaging system design and evaluation. Sarnoff Corporation, Princeton, NJ

    Google Scholar 

  97. Visual Information Systems Research Group (1997) Sarnoff JND vision model algorithm description and testing. Sarnoff Corporation, Princeton, NJ

    Google Scholar 

  98. Wainwright MJ, Simoncelli EP, Wilsky and AS (2001) Random cascades on wavelet trees and their use in analyzing and modeling natural images. Appl Comput Harmon Anal 11:89–123

    Google Scholar 

  99. Wandell BA (1995) Foundations of vision. Sinauer, Sunderland, MA

    Google Scholar 

  100. Wang Z, Bovik AC (2009) Mean squared error: Love it or leave it. IEEE Signal Process Mag 26(1):98–117

    Google Scholar 

  101. Wang Z, Bovik AC, Sheikh HR et al (2004) Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Google Scholar 

  102. Wang Z, Li Q (2007) Video quality assessment using a statistical model of human visual speed perception. J Opt Soc Amer A 24(12):B61–B69

    Google Scholar 

  103. Wang Z, Li Q (2011) Information content weighting for perceptual image quality assessment. IEEE Trans Image Process 20(5):1185–1198

    MathSciNet  Google Scholar 

  104. Wang Z, Lu L, Bovik AC (2004) Video quality assessment based on structural distortion measurement. Signal Process Image Commun 19(2):121–132

    Google Scholar 

  105. Watson AB (1987) The cortex transform: Rapid computation of simulated neural images. Comput Vision Graphics Image Process 39:311–327

    Google Scholar 

  106. Watson AB (1989) Receptive fields and visual representations. In: Proc SPIE 1077:190–197

    Google Scholar 

  107. Watson AB (1993) DCTune: A technique for visual optimization of DCT quantization matrices for individual images. In: Soc Inf Display Dig Tech Papers XXIV:946–949

    Google Scholar 

  108. Watson AB (ed) (1993) Digital images and human vision. MIT Press, Cambridge, MA

    Google Scholar 

  109. Watson AB, Solomon JA (1997) A model of visual contrast gain control and pattern masking. J Opt Soc Amer A 14(9):2379–2391

    Google Scholar 

  110. Watson AB, Taylor M, Borthwick R (1997) Image quality and entropy masking. In: Proc SPIE Int Soc Opt Eng 3016:2–12

    Google Scholar 

  111. Webster AA, Jones CT, Pinson MH et al (1993) An objective video quality assessment system based on human perception. In: Proc. SPIE-Human Vision, Visual Process Digital Display IV. 1913:15–26

    Google Scholar 

  112. Wei Z, Ngan KN (2009) Spatio-temporal just noticeable distortion profile for grey scale image/video in DCT domain. IEEE Trans Circuits Syst Video Technol 19(3):337–346

    Google Scholar 

  113. Winkler S (1999) A perceptual distortion metric for digital color video. In: Proc SPIE Human Vision and Electronic Imaging IV 3644:175–184

    Google Scholar 

  114. Winkler S, Min D (2013) Stereo/multiview picture quality: Overview and recent advances. Signal Process Image Commun 28:1358–1373

    Google Scholar 

  115. Winkler S, Mohandas P (2008) The evolution of video quality measurement: From PSNR to hybrid metrics. IEEE Trans Broadcast 54(3):660–668

    Google Scholar 

  116. Wu D, Tan DM, Baird M et al (2006) Perceptually lossless medical image coding. IEEE Trans Med Imaging 25(3):335–344

    Google Scholar 

  117. Wu HR, Lin W, Ngan KN (2014) Rate-perceptual-distortion optimisation (RpDO) based picture coding. In: Proc DSP 2014:777–782

    Google Scholar 

  118. Wu HR, Reibman AR, Lin W et al (2013) Perception-based visual signal compression and transmission. Proc IEEE 101(9):2025–2043

    Google Scholar 

  119. Wu HR, Rao KR (eds) (2006) Digital video image quality and perceptual coding. CRC Press, Boca Raton, FL

    Google Scholar 

  120. Wu HR, Yu Z, Qiu B (2002) Multiple reference impairment scale subjective assessment method for digital video. In: Proc DSP2002 1:185–189

    Google Scholar 

  121. Wu J, Lin W, Shi G (2014) Structural uncertainty based just noticeable difference estimation. In: Proc DSP2014

    Google Scholar 

  122. Yang XK, Lin WS, Lu ZK et al (2005) Just noticeable distortion model and its applications in video coding. Signal Process Image Commun 20(7):662–680

    Google Scholar 

  123. Yang F, Wan S (2012) Bitstream-based quality assessment for networked video: A review. IEEE Commun Mag 50(11):203–209

    Google Scholar 

  124. Yang F, Wan S, Xie Q et al (2010) No-reference quality assessment for networked video via primary analysis of bit stream. IEEE Trans Circuits Syst Video Technol 20(11):1544–1554

    Google Scholar 

  125. Yu Z, Wu HR, Winkler S et al (2002) Objective assessment of blocking artifacts for digital video with a vision model. Proc IEEE 90(1):154–169

    Google Scholar 

  126. Yuen M, Wu HR (1998) A survey of hybrid MC/DPCM/DCT video coding distortions. Signal Process 70:247–278

    MATH  Google Scholar 

  127. Zhang X, Wandell BA (1998) Color image fidelity metrics evaluated using image distortion maps. Signal Process 70(3):201–214

    MATH  Google Scholar 

  128. Zhang L, Zhang L, Mou X (2011) FSIM: A Feature Similarity Index for Image Quality Assessment. IEEE Trans Image Process 20(8):2378–2386

    MathSciNet  Google Scholar 

  129. Zilly F, Kluger J, Fauff P (2011) Production rules for stereo acquisition. Proc IEEE 99(4):590–606

    Google Scholar 

  130. Zujovic J, Pappas TN, Neuhoff DL (2013) Structural texture similarity metrics for image analysis and retrieval. IEEE Trans Image Process 22(7):2545–2558

    Google Scholar 

Download references

Acknowledgements

H.R. Wu thanks Dr D.M. Tan of HD2 Technologies for his inputs and valuable discussions during compilation of Chapter 1 and all his past and present colleagues who have contributed to the subject matters covered by Chapter 1.

Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, RMIT University, Swanston Street, Melbourne, Victoria, 3000, Australia

    Hong Ren Wu

Authors
  1. Hong Ren Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Ren Wu .

Editor information

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

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Wu, H.R. (2015). Introduction: State of the Play and Challenges of Visual Quality Assessment. 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_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10368-6_1

  • 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

Search

Navigation