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On the optimisation of nonlinear mapping functions towards high correlation of full-reference image quality metrics and their combinations with subjective evaluations

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Konferencja
Computer Applications in Electrical Engineering 2012 (23-24.04.2012; Poznań, Polska)
Języki publikacji
EN
Abstrakty
EN
One of the most relevant problems related to most of the objective full-reference image and video quality assessment methods is their correlation with subjective perception of various types of distortions. Since the relation between the subjective scores, typically expressed as Mean Opinion Scores (MOS) or Differential MOS, and most of the metrics is nonlinear, various mapping functions are used in order to linearize this relation e.g. logistic function recommended by the Video Quality Experts Group (VQEG). Such compensation can also be conducted using some other function e.g. polynomial or exponential ones. Nevertheless, the results obtained for each of available datasets differ and such approach cannot be considered as universal. One of possible solutions, proposed in some earlier papers, is the use of combined metrics, which have significantly higher linear correlation with MOS or DMOS values without the necessity of nonlinear mapping. The comparative analysis of the optimisation results for some “state-of-the-art” metrics and proposed combined ones is also provided in the paper.
Słowa kluczowe
Rocznik
Tom
Strony
416--430
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
  • West Pomerania University of Technology 71-126 Szczecin, ul. 26. Kwietnia 10
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] Okarma K., Combined Image Similarity Index, Optical Review, Volume 19, Number 5, pp. 249-254, 2012.
  • [8] Okarma K., Weighted Feature Similarity - a Nonlinear Combination of Gradient and Phase Congruency for Full-Reference Image Quality Assessment, Advances in Intelligent Systems and Computing, Volume 184, pp. 187-194, 2013.
  • [9] Okarma K., Hybrid Feature Similarity Approach to Full-Reference Image Quality Assessment, Lecture Notes in Computer Science, Volume 7595, pp. 212-219, 2012.
  • [10] 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.
  • [11] Sheikh H., Bovik A., Image information and visual quality, IEEE Transactions on Image Processing, Volume 15, Number 2, pp. 430-444, 2006.
  • [12] Sheikh H., Wang Z., Cormack L., Bovik A., LIVE Image Quality Assessment Database Release 2, Online, 2005. http://live.ece.utexas.edu/research/quality.
  • [13] VQEG, Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, phase I, Technical Report, 2000.
  • [14] VQEG, Final Report from the Video Quality Experts Group on the Validation of Objective Models of Video Quality Assessment, phase II, Technical Report, 2003.
  • [15] Wang Z., Bovik A., A Universal Image Quality Index, IEEE Signal Processing Letters, Volume 9, Number 3, pp. 81-84, 2002.
  • [16] 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.
  • [17] 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.
  • [18] 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.
  • [19] Zhang L., Zhang L., Mou X.: RFSIM: A Feature Based Image Quality Assessment Metric Using Riesz Transforms. Proceedings of the 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, China, 2010, pp. 321-324.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-25ccae34-8c97-45c1-884a-09ef48dab9cb
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