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Nonlinear mapping of the objective image quality assessment results in the aspect of correlation with subjective scores using various functions

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Warianty tytułu
Konferencja
Computer Applications in Electrical Engineering 2012 (23-24.04.2012; Poznań, Polska)
Języki publikacji
EN
Abstrakty
EN
An ideal objective image quality assessment method should be linearly correlated with subjective scores expressed as Mean Opinion Scores (MOS) or Differential MOS. Such scores are representative for the human perception of various types of image distortions and can be obtained from several image quality databases containing a number of images corrupted by various distortions and their MOS/DMOS values. Unfortunately, the relation between even the most recently proposed image quality assessment methods and subjective scores is nonlinear. In order to compensate this, the Video Quality Experts Group has recommended the use of the logistic function for the additional nonlinear mapping. Nevertheless, its parameters obtained for each database are different so some other functions, e.g. simple polynomials, can also be applied for this purpose. The analysis of the results obtained for currently available image databases is presented in the last part of the paper.
Rocznik
Tom
Strony
201--208
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • West Pomeranian University of Technology, Szczecin
Bibliografia
  • [1] Engelke U., Kusuma M., Zepernick H.J., Caldera M., Reduced-reference Metric Design for Objective Perceptual Quality Assessment in Wireless Imaging, Signal Processing: Image Communication, Volume 24, Number 7, pp. 525-547, 2009.
  • [2] Larson E., Chandler D., Most Apparent Distortion: Full-Reference Image Quality Assessment and the Role of Strategy, Journal of Electronic Imaging, Volume 19, Number 1, pp. 011006, 2010.
  • [3] Mansouri A., Mahmoudi-Aznaveh A., Torkamani-Azar F., Jahanshahi J., Image Quality Assessment Using the Singular Value Decomposition Theorem, Optical Review, Volume 16, Number 2, pp. 49-53, 2009.
  • [4] Okarma K., Combined Full-Reference Image Quality Metric Linearly Correlated with Subjective Assessment, Lecture Notes in Artificial Intelligence, Volume 6113, pp. 539-546, 2010.
  • [5] Okarma K., Video Quality Assessment Using the Combined Full-Reference Approach, Advances in Intelligent and Soft Computing, Volume 84, pp. 51-58, 2010.
  • [6] Okarma K., Colour Image Quality Assessment Using the Combined Full-Reference Approach, Advances in Intelligent and Soft Computing, Volume 95, pp. 287-296, 2011.
  • [7] 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, Volume 10, pp. 30-45, 2009.
  • [8] Sheikh H., Bovik A., Image information and visual quality, IEEE Transactions on Image Processing, Volume 15, Number 2, pp. 430-444, 2006.
  • [9] Sheikh H., Wang Z., Cormack L., Bovik A., LIVE Image Quality Assessment Database Release 2, Online, 2005. http://live.ece.utexas.edu/research/quality.
  • [10] VQEG, Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, phase I, Technical Report, Video Quality Experts Group, 2000.
  • [11] VQEG, Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, phase II, Technical Report, Video Quality Experts Group, 2003.
  • [12] Wang Z., Bovik A., A Universal Image Quality Index, IEEE Signal Processing Letters, Volume 9, Number 3, pp. 81-84, 2002.
  • [13] Wang Z., Bovik A., Sheikh H., Simoncelli E., Image quality assessment: From error measurement to structural similarity, IEEE Transactions on Image Processing, Volume 13, Number 4, pp. 600-612, 2004.
  • [14] Wang Z., Simoncelli E., Bovik A., Multi-Scale Structural Similarity for image quality assessment, Proceedings of the 37th IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, 2003.
  • [15] Zhang L., Zhang L., Mou X., Zhang D., FSIM: A Feature Similarity Index for Image Quality Assessment, IEEE Transactions on Image Processing, Volume 20, Number 8, pp. 2378-2386, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ba19a210-8700-4eb0-a01e-4a60ebc5e2fc
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