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A Fuzzy Regression Analysis Based No Reference Image Quality Metric

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Advances in Intelligent Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 320))

Abstract

In the paper quality metric of a test image is designed using fuzzy regression analysis by modeling membership functions of interval type 2 fuzzy set representing quality class labels of the image. The output of fuzzy regression equation is fuzzy number from which crisp outputs are obtained using residual error defined as the difference between observed and estimated output of the image. In order to remove human bias in assigning quality class labels to the training images, crisp outputs of fuzzy numbers are combined using weighted average method. Weights are obtained by exploring the nonlinear relationship between the mean opinion score (MOS) of the image and defuzzified output. The resultant metric has been compared with the existing quality metrics producing satisfactory result.

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References

  1. Chakraborty, C., Chakraborty, D.: Fuzzy Linear and Polynomial Regression Modelling of ‘if-Then’ Fuzzy Rulebase. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16(2), 219–232 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  2. Mendel, J.M., John, R.I., Liu, F.: Interval Type-2 Fuzzy Logic Systems Made Simple. IEEE T. Fuzzy Systems 14(6), 808–821 (2006)

    Article  Google Scholar 

  3. Wu, D., Mendel, J.M.: Perceptual reasoning using interval type-2 fuzzy sets: Properties. FUZZ-IEEE, 1219–1226 (2008)

    Google Scholar 

  4. Tanaka, H., Uejima, S., Asai, K.: Linear regression analysis with fuzzy model. IEEE Trans. Sys., Man. Cyber. 12, 903–907 (1982)

    Article  MATH  Google Scholar 

  5. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust Face Detection Using the Hausdorff Distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  6. Kadir, T., Brady, M.: Saliency, Scale and Image Description. International Journal of Computer Vision 45(2), 83–105 (2001)

    Article  MATH  Google Scholar 

  7. Tourancheau, S., Autrusseau, F., Sazzad, P.Z.M., Horita, Y.: Impact of subjective dataset on the performance of image quality metrics. In: ICIP, pp. 365–368 (2008)

    Google Scholar 

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

    Article  Google Scholar 

  9. Recommendation ITU-R BT.500-13: Methodology for the subjective assessment of the quality of television pictures (2012)

    Google Scholar 

  10. Kadir, T., Brady, M.: Scale, Saliency and Image Description. International Journal of Computer Vision 45(2), 83–105 (2001)

    Article  MATH  Google Scholar 

  11. Parzen, E.: On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics 33(3), 1065 (1962)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, MCKV Institute of Engineering, Liluah, Howrah, 711204, West Bengal, India

    Indrajit De

  2. Department of Computer Science and Technology, IIEST (Formerly BESUS), Shibpur, Howrah, West Bengal, India

    Jaya Sil

Authors
  1. Indrajit De
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  2. Jaya Sil
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Editor information

Editors and Affiliations

  1. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

  2. Indian Institute of Information Technology and Management- - Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  3. Faculty of Design, Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  4. Department of Information Systems and Operations Management, University of Florida, Warrington College of Business, Florida, Florida, USA

    Selwyn Piramuthu

  5. University of Applied Sciences, Institute for Information Systems, Olten, Switzerland

    Thomas Hanne

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© 2015 Springer International Publishing Switzerland

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De, I., Sil, J. (2015). A Fuzzy Regression Analysis Based No Reference Image Quality Metric. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-11218-3_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11217-6

  • Online ISBN: 978-3-319-11218-3

  • eBook Packages: EngineeringEngineering (R0)

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