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Measurement of noise in the Monte Carlo point sampling method

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Języki publikacji
EN
Abstrakty
EN
This paper gives a concise overview of the techniques we have used to find out the degree of measuring the quality of rendered images and a level of noise in particular. First part of the paper presents designed and conducted psychophysical experiment involving human subjective judgment. Then, two of the existing numerical image comparison methods are considered in the context of assessing the level of noise produced by global illuminations algorithms. The results of the participants’ subjective responses are correlated with the data obtained from objective mathematical metrics. The main goal of research was to determine the objective and perceptual measure of quality for images with fixed sampling strategy. The results will help to establish the measures of identifying the human perception in the assessment of images generated with global illumination algorithms.
Rocznik
Strony
15--19
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
autor
  • Institute of Information Technology, Technical University of Lodz, 215 Wólczańska, 90-924 Łódź, Poland, krzysztof.guzek@p.lodz.pl
Bibliografia
  • [1] D. Banks, “A basic guide to global illumination”, SIGGRAPH 98 1, CD-ROM (1998).
  • [2] G. Levine and S. Parkinson, Experimental Methods in Psychology, Lawrence Erlbaum Associates, New Jersey, 1994.
  • [3] K. Chiu and P. Shirley, “Rendering, complexity, and perception”, 5th Eurographics Workshop on Rendering 1, 19–33 (1994).
  • [4] A. McNamara, A. Chalmers, T. Troscianko, and E. Reinhard, “Fidelity of graphics reconstructions: a psychophysical investigation”, Proc. 9th Eurographics Rendering Workshop 1, 237–246 (1998).
  • [5] A. Janiak, W.A. Janiak, and R. Januszkiewicz, “Algorithms for parallel processor scheduling with distinct due windows and unit-time jobs”, Bull. Pol. Ac.: Tech. 57 (3), 209–215 (2009).
  • [6] P.M. Rademacher. “Measuring the perceived visual realism of images”, Ph.D. Thesis, Chapel Hill University, Chapel Hill, 2002.
  • [7] H.R. Sheikh, M.F. Sabir, and A.C. Bovik, “A statistical evaluation of recent full reference image quality assessment algorithms”, IEEE Trans. Image Processing 15 (11), 3440–3451 (2006).
  • [8] R. de Freitas Zampolo and R. Seara, “A comparison of image quality metric performances under practical conditions”, ICIP 2005 3, 1192–1195 (2005).
  • [9] H. Rushmeier, G. Ward, C. Piatko, P. Sanders, and B. Rust, “Comparing real and synthetic images: some ideas about metrics”, 6th Eurographics Workshop on Rendering 1, CD-ROM (1995).
  • [10] ˙I. Avcıbas¸, “Statistical evaluation of image quality measures”, J. Electronic Imaging 11, 206 (2002).
  • [11] A.B. Watson, “Toward a perceptual video quality metric”, Human Vision, Visual Processing, and Digital Display VIII 3299, 139–147 (1998).
  • [12] H. Górecki and M. Zaczyk, “Extremal dynamic errors in linear dynamic systems”, Bull. Pol. Ac.: Tech. 58 (1), 99–105 (2010).
  • [13] M. Bianchi, M. Boyle, and D. Hollingsworth, “A comparison of methods for trend estimation”, Applied Economics Letters 6 (2), 103–109 (1997).
  • [14] F. Xiao, “DCT-based video quality evaluation”, Final Project for EE392J Winter 1, CD-ROM (2000).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG8-0048-0033
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