Skip to main content
SpringerLink
Log in

Quality of Images

  • Chapter
  • First Online:
Data and Information Quality

Part of the book series: Data-Centric Systems and Applications ((DCSA))

  • 5160 Accesses

Abstract

An image is the result of the optical imaging process which maps physical scene properties onto a two-dimensional luminance distribution; it encodes important and useful information about the geometry of the scene and the properties of the objects located within this scene [339, 611, 687].

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-24106-7_15

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
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. Mittal A, Moorthy AK, Bovik AC (2012) No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing 21(12):4695–4708

    Article  MathSciNet  Google Scholar 

  2. Bartleson C (1982) The combined influence of sharpness and graininess on the quality of color prints. Journal of Photographic Science 30(2):33–38

    Google Scholar 

  3. Batini C, Scannapieco M (2006) Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications). Springer, New York

    MATH  Google Scholar 

  4. Batini C, Barone D, Cabitza F, Ciocca G, Marini F, Pasi G, Schettini R (2008) Toward a unified model for information quality. In: Proceedings of the International Workshop on Quality in Databases and Management of Uncertain Data, Auckland, August 2008, pp 113–122

    Google Scholar 

  5. Bhattacharya S, Sukthankar R, Shah M (2011) A holistic approach to aesthetic enhancement of photographs. ACM Transactions on Multimedia Computing, Communications, and Applications 7S:21:1–21:21

    Google Scholar 

  6. Bianco S, Ciocca G, Marini F, Schettini R (2009) Image quality assessment by preprocessing and full reference model combination. In: Image Quality and System Performance VI. SPIE, vol 7242, pp 1–9

    Google Scholar 

  7. Bibliographic Center for Research CDP Digital Imaging Best Practices Working Group (2008) Digital Imaging Best Practices, Version 2.0. Bibliographic Center for Research, URL http://books.google.it/books?id=vjeEXwAACAAJ

  8. Brandao T, Queluz MP (2008) No-reference image quality assessment based on dct domain statistics. Signal Processing 88(4):822–833

    Article  MATH  Google Scholar 

  9. Callet P, Autrusseau F (2005) Subjective quality assessment IRC-CyN/IOVC database. http://www.irccyn.ec-nantes.fr/ivcdb/

  10. Carnec M, Callet PL, Barba D (2008) Objective quality assessment of color images based on a generic perceptual reduced reference. Signal Processing: Image Communication 23(4):239–256

    Google Scholar 

  11. Chandler D, Hemami S (2007) A57 image database. http://foulard.ece.cornell.edu/dmc27/vsnr/vsnr.html

  12. Chandler DM (2013) Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Processing

    Google Scholar 

  13. Chikkerur S, Sundaram V, Reisslein M, Karam L (2011) Objective video quality assessment methods: a classification, review, and performance comparison. IEEE Transactions on Broadcasting 57(2):165–182

    Article  Google Scholar 

  14. Ciancio A, da Costa A, da Silva E, Said A, Samadani R, Obrador P (2009) Objective no-reference image blur metric based on local phase coherence. Electronics Letters 45(23):1162–1163

    Article  Google Scholar 

  15. Corchs S, Gasparini F, Marini F, Schettini R (2011) Image quality: a tool for no-reference assessment methods. Image Quality and System Performance VIII 7867(1):786712

    Article  Google Scholar 

  16. Corchs S, Gasparini F, Marini F, Schettini R (2012) A sharpness measure on automatically selected edge segments. Image Quality and System Performance IX 8293(1):82930A

    Article  Google Scholar 

  17. Corchs S, Gasparini F, Schettini R (2014) No reference image quality classification for jpeg-distorted images. Digital Signal Processing 30:86–100

    Article  Google Scholar 

  18. Corchs S, Gasparini F, Schettini R (2014) Noisy images-jpeg compressed: subjective and objective image quality evaluation. In: IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics

    Google Scholar 

  19. Corner BR, Narayanan RM, Reichenbach SE (2003) Noise estimation in remote sensing imagery using data masking. International Journal of Remote Sensing 24(4):689–702

    Article  Google Scholar 

  20. Datta R, Joshi D, Li J, Wang JZ (2006) Studying aesthetics in photographic images using a computational approach. In: Proceedings of the ECCV, pp 7–13

    Google Scholar 

  21. Eckbert M, Bradley A (1998) Perceptual quality metrics applied to still image compression. Signal Processing 70(3):177–200

    Article  MATH  Google Scholar 

  22. Engeldrum PG (2001) Psychometric scaling: avoiding the pitfalls and hazards. In: IS&T’s 2001 PICS Conference Proceedings, pp 101–107

    Google Scholar 

  23. Frey F, Reilly J, of Technology Image Permanence Institute RI (1999) Digital Imaging for Photographic Collections: Foundations for Technical Standards. Image Permanence Institute, URL http://books.google.it/books?id=75QrAQAAMAAJ

    Google Scholar 

  24. Gonzales RC, Woods R (2008) Digital Image Processing. Prentice Hall, Englewood Cliffs

    Google Scholar 

  25. H Tang NJ, Kapoor A (2011) Learning a blind measure of perceptual image quality. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 305–312

    Google Scholar 

  26. Hasler D, Süsstrunk SE (2003) Measuring colorfulness in natural images. Human Vision and Electronic Imaging VIII 5007:87–95

    Article  Google Scholar 

  27. I3A (2007) Fundamentals and review of considered test methods. CPIQ Initiative Phase 1 White Paper

    Google Scholar 

  28. Imatest (2010) Digital Image Quality Testing. http://www.imatest.com

  29. ISO (2005) Image Technology Colour Management - Architecture, Profile Format and Data Structure - Part 1: Based on ICC.1:2004-10. ISO 15076-1. ISO, 2005

    Google Scholar 

  30. ISO (accessed February 09, 2012) Information technology – Multimedia content description interface – Part 1: Systems. URL http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=34228

  31. ITU (2002) Methodology for the subjective assessment of the quality for television pictures. Technical report, ITU-R Rec. BT. 500-11

    Google Scholar 

  32. Janssen T (2001) Computational Image Quality. SPIE Press

    Google Scholar 

  33. Janssen T, Blommaert F (2000) A computational approach to image quality. Displays 21:129–142

    Article  Google Scholar 

  34. Jayaraman D, Mittal A, Moorthy A, Bovik A (2012) Objective quality assessment of multiply distorted images. In: Proceedings of the of the Asilomar Conference on Signals, Systems and Computers, pp 1693–1697

    Google Scholar 

  35. Keelan BW (2002) Handbook of Image Quality: Characterization and Prediction. CRC Press, Boca Raton

    Book  Google Scholar 

  36. Kusuma T, Zepernick HJ (2003) A reduced-reference perceptual quality metric for in-service image quality assessment. In: Joint First Workshop on Mobile Future and Symposium on Trends in Communications (SympoTIC ’03), pp 71–74

    Google Scholar 

  37. Larson EC, Chandler DM (2010) Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19(1):011006-1–011006-21

    Google Scholar 

  38. LIVE video (2009) Live video quality database. URL http://live.ece.utexas.edu/research/quality/live_video.html

  39. Lundstrom C (2006) Technical report: Measuring digital image quality. Tech. rep., Linkoping UniversityLinkoping University, Visual Information Technology and Applications (VITA), The Institute of Technology

    Google Scholar 

  40. MacDonald L, Jacobson R (2006) Assessing image quality. In: Digital Heritage: Applying Digital Imaging to Cultural Heritage. Elsevier Butterworth-Heinemann

    Google Scholar 

  41. Marziliano P, Dufaux F, Winkler S, Ebrahimi T (2002) A no-reference perceptual blur metric. In: IEEE 2002 International Conference on Image Processing, pp 57–60

    Google Scholar 

  42. Moorthy A, Bovik A (2011) Visual quality assessment algorithms: what does the future hold? Multimedia Tools and Applications 51:675–696

    Article  Google Scholar 

  43. Nishiyama M, Okabe T, Sato I, Sato Y (2011) Aesthetic quality classification of photographs based on color harmony. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 33–40

    Google Scholar 

  44. Pessoa A, Falcao A, e Silva A, Nishihara R, Lotufo R (1998) Video quality assessment using objective parameters based on image segmentation. In: Proceedings of the SBT/IEEE International Telecommunications Symposium (ITS ’98), vol 2, pp 498–503

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  47. de Ridder H, Endrikhovski S (2002) Image quality is fun: reflections on fidelity, usefulness and naturalness. SID Symposium Digest of Technical Papers 33:986–989

    Article  Google Scholar 

  48. Rouse DM, Hemami SS (2008) Analyzing the role of visual structure in the recognition of natural image content with multi-scale SSIM. In: Proceedings of the SPIE: HVEI XIII, vol 6806, pp 1–14

    Google Scholar 

  49. Saha S, Vemuri R (2000) An analysis on the effect of image activity on lossy coding performance. In: Proceedings of the 2000 IEEE International Symposium on Circuits and Systems (ISCAS 2000), Geneva, vol 3, pp 295–298

    Google Scholar 

  50. Sazzad Z, Kawayoke Y, Horita Y (2000) Mict image quality evaluation database, http://mict.eng.u-toyama.ac.jp/mict/index2.html

  51. Schettini R, Gasparini F (2009) A review of redeye detection and removal in digital images through patents. Recent Patents on Electrical Engineering 2(1):45–53

    Article  Google Scholar 

  52. Seshadrinathan K, Bovik AC (2010) Motion tuned spatio-temporal quality assessment of natural videos. Transaction Imgage Processing 19(2):335–350

    Article  MathSciNet  Google Scholar 

  53. Sharma G (2002) Digital Color Imaging Handbook. CRC Press, Boca Raton

    Book  Google Scholar 

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

    Article  Google Scholar 

  55. Sheikh HR, Wang Z, Cormack L, Bovik AC (2005) LIVE Image Quality Assessment Database Release 2

    Google Scholar 

  56. Sikora T (2001) The MPEG-7 visual standard for content description-an overview. IEEE Transactions on Circuits and Systems for Video Technology 11(6):696–702

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  58. Soundararajan R, Bovik A (2013) Video quality assessment by reduced reference spatio-temporal entropic differencing. IEEE Transactions on Circuits and Systems for Video Technology 23(4):684–694

    Article  Google Scholar 

  59. Suthaharan S (2009) No-reference visually significant blocking artifact metric for natural scene images. Signal Processing 89(8):1647–1652

    Article  MATH  Google Scholar 

  60. TASI (1979) Technical advisory service for images

    Google Scholar 

  61. Thurstone LL (1927) A law of comparative judgement. Psychological Review 34:273–286

    Article  Google Scholar 

  62. Torgerson W (1958) Theory and Methods of Scaling. Wiley, New York

    Google Scholar 

  63. Tourancheau S, Autrusseau F, Sazzad Z, Horita Y (2008) Impact of subjective dataset on the performance of image quality metrics. In: 15th IEEE International Conference on Image Processing (ICIP 2008), pp 365–368

    Google Scholar 

  64. US National Archives (accessed February 09, 2012) Technical guidelines for digitizing archival materials for electronic access: creation of production master files - raster images. URL http://www.archives.gov/preservation/technical/guidelines.html

  65. VQEG (2000) Final report from the video quality experts group on the validation of objective models of video quality assessment, URL http://www.vqeg.org/

  66. VQEG (2000) Vqeg frtv phase 1 database, URL ftp://ftp.crc.ca/crc/vqeg/TestSequences/

  67. Wang Z, Simoncelli EP (2005) Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In: Proceedings of SPIE Human Vision and Electronic Imaging, vol 5666, pp 149–159

    Google Scholar 

  68. Wang Z, Bovik A, Evans B (2000) Blind measurement of blocking artifacts in images. In: Proceedings of the IEEE International Conference Image Processing, pp 981–984

    Google Scholar 

  69. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4):600–612

    Article  Google Scholar 

  70. Watson AB, Borthwick R, Taylor M (1997) Image quality and entropy masking. In: SPIE Human Vision and Electronic Imaging Conference, vol 3016, pp 2–12

    Google Scholar 

  71. Wee CY, Paramesran R, Mukundan R, Jiang X (2010) Image quality assessment by discrete orthogonal moments. Pattern Recognition 43(12):4055–4068

    Article  MATH  Google Scholar 

  72. Xue W, Zhang L, Mou X, Bovik AC (2014) Gradient magnitude similarity deviation: a highly efficient perceptual image quality index. IEEE Transactions on Image Processing 23(2):684–695

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  74. Yendrikhovskij S (1999) Image quality: between science and fiction. In: PICS, pp 173–178

    Google Scholar 

  75. Zhang X, Wandell BA (1997) A spatial extension of cielab for digital color-image reproduction. Journal of the Society for Information Display 5(1):61–63

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Università degli Studi di Milano-Biccoca, Milan, Italy

    Carlo Batini

Authors
  1. Gianluigi Ciocca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Silvia Corchs
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesca Gasparini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carlo Batini
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Raimondo Schettini
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Ciocca, G., Corchs, S., Gasparini, F., Batini, C., Schettini, R. (2016). Quality of Images. In: Data and Information Quality. Data-Centric Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-24106-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24106-7_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24104-3

  • Online ISBN: 978-3-319-24106-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation