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Metrics Fusion

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Visual Quality Assessment by Machine Learning

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSSIGNAL))

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Abstract

There have been an abundance of developed image quality assessment (IQA) metrics during the last decade. However, these is not an individual one, whose performance always tops the performance ranking list on all subjective databases and for all distortions. The combination of multiple IQA metrics is expected to be better than each of them individually used. Two metric fusion frameworks are introduced in this chapter. The one introduces a multi-method fusion (MMF), which gives a MMF score by using a nonlinear combination of the scores computed from the multiple IQA metrics for fusion. The combination is achieved by training a set of weights using support vector regression. The other one presents an ensemble-based framework. In this framework, some features are extracted from the existing IQA metrics. These features are trained to be as basic image quality scorers (BIQSs). For copying with specific distortion types, some advanced features are trained to be as advanced image quality scorers (AIQSs). In addition, two statistical testing methods are employed to do scorer selection. Finally, a machine learning approach is adopted as a score fuser to combine all outputs from the selected scorers.

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References

  1. Tampere Image Database. http://www.ponomarenko.info/tid2008.htm

  2. Basak D, Pal S, Patranabis DS (2007) Support vector regression. Neural Inf Process Lett Rev 11(10):203–224

    Google Scholar 

  3. Hsu C-W, Chang C-C, Lin C-J (2010) A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, Taipei, Taiwan

    Google Scholar 

  4. Bishop CM (2006) Pattern recognition and machine learning. Springer, New York

    MATH  Google Scholar 

  5. Chang C-C, Lin C-J (2011) LIBSVM : a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27:1–27:27

    Google Scholar 

  6. A57 Database. http://foulard.ece.cornell.edu/dmc27/vsnr/vsnr.html

  7. Categorical Image Quality (CSIQ) Database. http://vision.okstate.edu/csiq

  8. IVC Image Quality Database. http://www2.irccyn.ec-nantes.fr/ivcdb

  9. LIVE Image Quality Assessment Database. http://live.ece.utexas.edu/research/quality/subjective.htm

  10. Toyoma Database. http://mict.eng.utoyama.ac.jp/mictdb.html

  11. Wang Z, Sheikh HR, Bovik AC (2002) No-reference perceptual quality assessment of JPEG compressed images. Proceedings of IEEE international conference of image process, Sept 2002, pp 477–480

    Google Scholar 

  12. Ong E, Lin W, Lu Z, Yang X, Yao S, Pan F, Jiang L, Moschetti F (2003) A no-reference quality metric for measuring image blur. In: Proceedings of IEEE 7th international symposium signal process application, July 2003, pp 469–472

    Google Scholar 

  13. Liu T-J, Lin WS, Kuo C-CJ (2013) Image quality assessment using multi-method fusion. IEEE Trans Image Process 22(5):1793–1806

    Google Scholar 

  14. Liu T-J, Lin W, Kuo C-CJ (2015) A ParaBoost method to image quality assessment, IEEE Trans Neural Netw Learn Syst, accepted

    Google Scholar 

  15. Liu T-J, Lin W, Kuo C-CJ (2011) A multi-metric fusion approach to visual quality assessment. In: Proceedings of IEEE 3rd international workshop quality multimedia experience, Sept 2011, pp 72–77

    Google Scholar 

  16. Luo H (2004) A training-based no-reference image quality assessment algorithm. In: Proceedings of IEEE international conference of image process, Oct 2004, pp 2973–2976

    Google Scholar 

  17. Suresh S, Babu V, Sundararajan N (2006) Image quality measurement using sparse extreme learning machine classifier. In: Proceedings of IEEE 9th international conference control, automation, robotics and vision, Dec 2006, pp 1–6

    Google Scholar 

  18. Suresh S, Babu V, Kim HJ (2009) No-reference image quality assessment using modified extreme learning machine classifier. J Appl Soft Comput 9(2):541–552

    Article  Google Scholar 

  19. Narwaria M, Lin W (2010) Objective image quality assessment based on support vector regression. IEEE Trans Neural Netw 21(3):515–519

    Article  Google Scholar 

  20. Moorthy AK, Bovik AC (2010) A two-step framework for constructing blind image quality indices. IEEE Signal Process Lett 17(5):513–516

    Article  Google Scholar 

  21. Jin L, Egiazarian K, Kuo C-CJ (2012) Perceptual image quality assessment using block-based multi-metric fusion (BMMF). In: ICASSP 2012

    Google Scholar 

  22. Wang Z, Simoncelli E, Bovik A, Matthews M (2003) Multiscale structural similarity for image quality assessment. In: Proceedings of IEEE asilomar conference signals, system computer, Nov 2003, pp 1398–1402

    Google Scholar 

  23. Sheikh HR, Bovik AC, de Veciana G (2005) An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans Image Process 14(12):2117–2128

    Article  Google Scholar 

  24. Leontaris A, Cosman PC, Reibman AR (2007) Quality evaluation of motion-compensated edge artifacts in compressed video. IEEE Trans Image Process 16(4):943–956

    Article  MathSciNet  Google Scholar 

  25. Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F (2009) TID2008–a database for evaluation of full-reference visual quality assessment metrics. Adv Mod Radioelectron 10:30–45

    Google Scholar 

  26. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Article  Google Scholar 

  27. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Int 24(7):971–987

    Google Scholar 

  28. Pratt WK (2007) Digital image processing, 4th edn. Wiley, New York

    Book  MATH  Google Scholar 

  29. Gonzalez RC, Woods RE (2007) Digital image processing, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  30. 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

    Article  MathSciNet  Google Scholar 

  31. Damera-Venkata N, Kite T, Geisler W, Evans B, Bovik AC (2000) Image quality assessment based on a degradation model. IEEE Trans Image Process 9(4):636–650

    Article  Google Scholar 

  32. Tampere Image Database 2013. http://www.ponomarenko.info/tid2013.htm

  33. Polikar R (2006) Ensemble based systems in decision making. IEEE Circuits Syst Mag 6(3):21–45

    Google Scholar 

  34. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Google Scholar 

  35. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  36. Glantz SA (2005) Primer of biostatistics, 6th edn. McGraw Hill, New York

    Google Scholar 

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Authors and Affiliations

  1. National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China

    Long Xu

  2. Nanyang Technological University, Singapore, Singapore

    Weisi Lin

  3. University of Southern California, Los Angeles, CA, USA

    C.-C. Jay Kuo

Authors
  1. Long Xu
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  2. Weisi Lin
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  3. C.-C. Jay Kuo
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Correspondence to Long Xu .

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Xu, L., Lin, W., Kuo, CC.J. (2015). Metrics Fusion. In: Visual Quality Assessment by Machine Learning. SpringerBriefs in Electrical and Computer Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-287-468-9_5

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  • DOI: https://doi.org/10.1007/978-981-287-468-9_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-467-2

  • Online ISBN: 978-981-287-468-9

  • eBook Packages: EngineeringEngineering (R0)

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