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Abstract

Design of visual environment is a complex issue involving lighting sources, luminous properties of surfaces, brightness and glare. Digital image processing software can simulate lighting designs to ensure the right amount of light and luminous quality of the visual environment. Even though there are several means about how to determine the quality of the visual environment, this study adopted high dynamic range (HDR) images captured in real-life environment. The HDR imaging has been used for research purposes to analyse discomfort glare and extract luminance on the scene. However, numbers of previous researches reveal that the capacity of HDR imaging technology for luminance acquisition might be dependent upon types of camera and the exposed ranges of images. This paper aims to verify the accuracy and pragmatic reliability of HDR image tools such as LMK and Photolux. The reflectance percentages measured by LMK and Photolux were compared to the absolute values presented by Pantone hue, value and chroma. A series of Pantone colour, yellow, warm red, rubine red, rhodamine red, purple, reflex blue, process blue and green were adopted and with varying the mixture ratio of whiteness. A variation on this type of analysis can be used to validate the design and address visual problem areas such as the detection of potential glare sources.

Keywords

Image processing HDR Luminance mapping Visual analysis Pantone colour

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References

Larson

Rushmeier

Piatko

. A visibility matching tone reproduction operator for high dynamic range scenes. IEEE Trans Vis Comput Graphics 1997; 3: 4–4.

Ward G and Simmons M: Subband encoding of high dynamic range imagery. In: APGV’04: Proceedings of the 1st symposium on applied perception in graphics and visualization, ACM Press, Los Angeles, LA, USA, 31 July 2004, pp.836–844.

Seetzen H, Heidrich W, Stuerzlinger W, Ward G, Whitehead L, Trentacoste M, Ghosh A and Vorozcovs A. High dynamic range display systems. In: ACM transactions on graphics, Los Angeles, LA, USA, 8 August 2004, pp.760–768.

Li Y, Sharan L and Adelson EH. Compressing and companding high dynamic range images with subband architectures. Cambridge, MA: Department of Brain and Cognitive Sciences, and Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, 2005, vol. 7, pp.836–844.

Tumblin J and Rushmeier H. Tone reproduction for realistic images. IEEE Comput Graphics Appl 1993; 13(6): 42–48.

Nakamae E, Kaneda K, Okamoto T and Nishita T. A lighting model aiming at drive simulators. In: Proceedings of ACM SIGGRAPH ’90, Dallas, TX, USA, 6–10 August 1990, vol. 3, pp.395–404.

Spencer G, Shirley P, Zimmerman K and Greenberg DP. Physically-based glare effects for digital images. In: Proceedings of ACM SIGGRAPH’95, Los Angeles, LA, USA, 6–10 August 1995, pp.325–334.

Ferwerda J, Pattanaik S, Shirley P and Greenberg DP. A model of visual adaptation for realistic image synthesis. In: Proceedings of ACM SIGGRAPH’96, New Orleans, LA, USA, 4–9 August, 1996, pp.249–258.

Mantiuka R, Dalyb SJ, Myszkowskia K and Seidela HP. aMPIInformatik, Stuhlsatzenhausweg: Predicting Visible Differences in High Dynamic Range Images – Model and its Calibration 85, 66123 Saarbrücken, Germany. bSharp Laboratories of America, 5750 NW Pacific Rim Blvd Camas, WA 98607, USA/SPIE-IS&T/Vol. 5666, 2005, pp.204–214.

10.

Robertson M, Borman S and Stevenson R: Dynamic range improvement through multiple exposures. In: Proceedings of the 1999 International Conference on Image Processing (ICIP-99), Kobe, Japan, 24 October 1999, pp.159–163.

11.

Devlin K, Chalmers A, Wilkie A and Purgathofer W. Tone reproduction and physically based spectral rendering. In: Eurographics 2002: State of the art reports, 2002, pp.101–123.

12.

Drago F, Martens W, Myszkowski K and Seidel HP. Perceptual evaluation of tone mapping operators with regard to similarity and preference. Technical Report MPI-I-2002-4-002, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.8847&rep=rep1&type=pdf (accessed 30 June 2013).

13.

Kuang J, Yamagishi H, Johnson G and Fairchild M. Testing HDR rendering algorithms. In: IS&T/SID color imaging conference, Scottsdale, AZ, 9–12 November 2004, pp.315–320.

14.

Nadenau M. Integration of human color vision models into high quality image compression. PhD Thesis, École Polytechnique Fédéral Lausane, 2000.

15.

Cai

Chung

. Improving the quality of high dynamic range images. Lighting Res Technol 2011; 43: 87–102.

16.

Cai

. High dynamic range photogrammetry for synchronous luminance and geometry measurement. Lighting Res Technol 2012; 45: 2–2.

17.

Cai H. Camera aided luminance measurement of the luminous surfaces of different light sources. In: ASCE proceedings of the 2011 architectural engineering national conference, 30 April 2011, Oakland, CA, d 20110000. American Society of Civil Engineers, pp.272–279.

18.

Reinhart

Wienold

. The daylighting dashboard – a simulation based design analysis for daylit spaces. Building Environ 2011; 46: 386–396.

19.

Wymelenberg KV and Inanici M. A study of luminance distribution patterns and occupant preference in daylit offices. In: PLEA2009 – 26th conference on passive and low energy architecture, Quebec City, Canada, 21 June 2009, pp.22–24.

20.

Anaokar

Moeck

. Validation of high dynamic range imaging to luminance measurement. LEUKOS 2005; 2: 133–144.

21.

Bar Code Graphics, Inc. Guide to color selection, http://www.barcode-us.com/sites/default/files/bcgcolorguide.pdf (accessed date 30 June 2013).

Comparative verification of high dynamic range image tools for luminous analysis of visual environment

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