Skip to main content
SpringerLink
Log in

A New Color Image Database TID2013: Innovations and Results

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8192))

Abstract

A new database of distorted color images called TID2013 is designed and described. In opposite to its predecessor, TID2008, this database contains images with five levels of distortions instead of four used earlier and a larger number of distortion types (24 instead of 17). The need for these modifications is motivated and new types of distortions are briefly considered. Information on experiments already carried out in five countries with the purpose of obtaining mean opinion score (MOS) is presented. Preliminary results of these experiments are given and discussed. Several popular metrics are considered and Spearman rank order correlation coefficients between these metrics and MOS are presented and discussed. Analysis of the obtained results is performed and distortion types difficult for assessment by existing metrics are noted.

The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Keelan, B.W.: Handbook of Image Quality. Marcel Dekker, Inc., New York (2002)

    Book  Google Scholar 

  2. Wu, H.R., Lin, W., Karam, L.: An Overview of Perceptual Processing for Digital Pictures. In: Proceedings of International Conference on Multimedia and Expo Workshops, Melbourne, pp. 113–120 (2012)

    Google Scholar 

  3. Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19(1), 011006 (2010)

    Article  Google Scholar 

  4. Ponomarenko, N., Krivenko, S., Lukin, V., Egiazarian, K.: Lossy Compression of Noisy Images Based on Visual Quality: A Comprehensive Study. EURASIP Journal on Advances in Signal Processing, 13 (2010), doi:10.1155/2010/976436

    Google Scholar 

  5. Carli, M.: Perceptual Aspects in Data Hiding. Thesis for the degree of Doctor of Technology, Tampere University of Technology (2008)

    Google Scholar 

  6. 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 

  7. Fevralev, D., Lukin, V., Ponomarenko, N., Abramov, S., Egiazarian, K., Astola, J.: Efficiency analysis of DCT-based filters for color image database. In: Proceedings of SPIE Conference Image Processing: Algorithms and Systems VII, San Francisco, vol. 7870 (2011)

    Google Scholar 

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

    Article  Google Scholar 

  9. Chandler, D.M.: Seven Challenges in Image Quality Assessment: Past, Present and Future Research. In: ISNR Signal Processing, vol. 2913, pp. 1–53 (2013)

    Google Scholar 

  10. Jin, L., Egiazarian, K., Jay Kuo, C.-C.: Perceptual Image Quality Assessment Using Block-Based Milti-Metric Fusion (BMMF). In: Proceedings of ICASSP, Kyoto, pp. 1145–1148 (2012)

    Google Scholar 

  11. Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE Image Quality Assessment Database Release 2, http://live.ece.utexas.edu/research/quality/subjective.htm

  12. 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), 3441–3452 (2006)

    Article  Google Scholar 

  13. Horita, Y., Shibata, K., Parvez Saddad, Z.M.: Subjective quality assessment toyama database, http://mict.eng.u-toyama.ac.jp/mict/

  14. Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., Battisti, F.: TID2008 - A Database for Evaluation of Full-Reference Visual Quality Assessment Metrics. Advances of Modern Radioelectronics 10, 30–45 (2009)

    Google Scholar 

  15. Uss, M., Vozel, B., Lukin, V., Abramov, S., Baryshev, I., Chehdi, K.: Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters. EURASIP Journal on Advances in Signal Processing, 961–964 (2011)

    Google Scholar 

  16. Lukin, V., Ponomarenko, N., Egiazarian, K.: HVS-Metric-Based Performance Analysis of Image Denoising Algorithms. In: Proceedings of EUVIP, pp. 156–161 (2011)

    Google Scholar 

  17. Vu, C.T., Phan, T.D., Chandler, D.M.: S3: a Spectral and Spatial Measure of Local Perceived Sharpness in Natural Images. IEEE Transactions on Image Processing 21(3), 934–945 (2012)

    Article  MathSciNet  Google Scholar 

  18. Kendall, M.G.: The advanced theory of statistics, vol. 1. Charles Griffin & Company limited, London (1945)

    Google Scholar 

  19. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: IEEE Asilomar Conference on Signals, Systems and Computers, pp. 1398–1402 (2003)

    Google Scholar 

  20. Jin, L., Egiazarian, K., Jay Kuo, C.-C.: Performance comparison of decision fusion strategies in BMMF-based image quality assessment. In: Proceedings of APSIPA, Hollywood, pp. 1–4 (2012)

    Google Scholar 

  21. Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20(5), 2378–2386 (2011)

    Article  MathSciNet  Google Scholar 

  22. Ponomarenko, N., Eremeev, O., Lukin, V., Egiazarian, K., Carli, M.: Modified image visual quality metrics for contrast change and mean shift accounting. In: Proceedings of CADSM, Polyana-Svalyava, pp. 305–311 (2011)

    Google Scholar 

  23. Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging 19(1), 011006 (2010)

    Article  Google Scholar 

  24. Pedersen, M., Bonnier, N., Hardeberg, J.Y., Albregtsen, F.: Attributes of Image Quality for Color Prints. Journal of Electronic Imaging 19(1), 011016-1–011016-13 (2010)

    Google Scholar 

  25. Hassan, M., Bhagvati, C.: Structural Similarity Measure for Color Images. International Journal of Computer Applications 43(14), 7–12 (2012)

    Article  Google Scholar 

  26. Ponomarenko, N.N., Lukin, V.V., Ieremeyev, O.I., Egiazarian, K., Astola, J.: Visual quality analysis for images degraded by different types of noise. In: Proceedings of SPIE Symposium on Electronic Imaging, San Francisco, vol. 8655, p. 12. SPIE (2013)

    Google Scholar 

  27. Oh, B.T., Jay Kuo, C.-C., Sun, S., Lei, S.: Film Grain Noise Modeling in Advanced Video Coding, SPIE Proceedings, Vol. In: SPIE Proceedings, San Jose, vol. 6508, p. 12 (2007)

    Google Scholar 

  28. Petrescu, D., Pincenti, J.: Quality and noise measurements in mobile phone video capture. In: SPIE Proceedings, San Francisco, vol. 7881, p. 14 (2011)

    Google Scholar 

  29. Danielyan, A., Foi, A., Katkovnik, V., Egiazarian, K.: Spatially adaptive filtering as regularization in inverse imaging: compressive sensing, upsampling, and super-resolution. In: Milanfar, P. (ed.) Super-Resolution Imaging, CRC Press / Taylor & Francis (2010)

    Google Scholar 

  30. Paredes, J.L., Arce, G.R.: Compressive Sensing Signal Reconstruction by Weighted Median Regression Estimate. IEEE Transactions on Signal Processing 59(6), 2585–2601 (2011)

    Article  MathSciNet  Google Scholar 

  31. Damera-Venkata, N., Kite, T., Geisler, W., Evans, B., Bovik, A.: Image Quality Assessment Based on a Degradation Model. IEEE Transactions on Image Processing 9, 636–650 (2000)

    Article  Google Scholar 

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

    Article  Google Scholar 

  33. Sheikh, H.R., Bovik, A.C.: Image Information and Visual Quality. IEEE Transactions on Image Processing 15, 430–444 (2006)

    Article  Google Scholar 

  34. Chandler, D.M., Hemami, S.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 

  35. Mitsa, T., Varkur, K.: Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms. In: IEEE International Conference on Acoustic, Speech, and Signal Processing, Minneapolis, vol. 5, pp. 301–304 (1993)

    Google Scholar 

  36. Gaubatz, M.: Metrix MUX Visual Quality Assessment Package, http://foulard.ece.cornell.edu/gaubatz/metrix_mux

  37. 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, Scottsdale, p. 4 (2006)

    Google Scholar 

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

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept of Transmitters, Receivers and Signal Processing, National Aerospace University, 17 Chkalova St, 61070, Kharkov, Ukraine

    Nikolay Ponomarenko, Oleg Ieremeiev & Vladimir Lukin

  2. Institute of Signal Processing, Tampere University of Technology, P.O.Box-553, FIN-33101, Tampere, Finland

    Lina Jin, Karen Egiazarian & Jaakko Astola

  3. University of Rennes 1 - IETR, CS 80518, 22305, Lannion Cedex, France

    Benoit Vozel & Kacem Chehdi

  4. University of Rome III, via Ostiense , 161, Rome, Italy

    Marco Carli & Federica Battisti

  5. Media Communications Lab, USC Viterbi School of Engineering, SAL 300, USA

    C. -C. Jay Kuo

Authors
  1. Nikolay Ponomarenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oleg Ieremeiev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vladimir Lukin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lina Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Karen Egiazarian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jaakko Astola
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Benoit Vozel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kacem Chehdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Marco Carli
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Federica Battisti
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. C. -C. Jay Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. DGA, 7-9 rue des mathurins, 92 221, Bagneux, France

    Jacques Blanc-Talon

  2. Institute of Control and Information Engineering, Poznań University of Technology, Piotrowo 3A, 60-965, Poznań, Poland

    Andrzej Kasinski

  3. Telecommunications and Information Processing (TELIN), Ghent University, St.-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Wilfried Philips

  4. CSIRO ICT Centre, Epping, Po Box 76, 1710, Sydney, NSW, Australia

    Dan Popescu

  5. University of Antwerp, Universiteitsplein 1, Building N., 2610, Wilrijk, Antwerp, Belgium

    Paul Scheunders

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ponomarenko, N. et al. (2013). A New Color Image Database TID2013: Innovations and Results. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02895-8_36

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02894-1

  • Online ISBN: 978-3-319-02895-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation