Usability of Digital Cameras for Verifying Physically Based ...

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Figure 61: Example of spectral sensitivity curves of a CYGM CCD sensor. The main problem of this approach is that of metamerism. A single RGB value can be caused by an infinitely large number of spectra. The mapping is of the type R 3 → R n , n being the number of samples of a spectrum, usually n > 3. The set of equations is under-determined, therefore we get a non-trivial solution space. Let W be a transformation matrix that gives us the point in this space that has the smallest Frobenius norm. Let Si be the spectrum that caused the RGB value and let Sj be the reconstructed spectrum. There is no way to tell whether Sj is equal to the original spectrum Si, or not. It is possible, that if Si �= Sj, Sj is a metamere of Si as far as the camera is concerned. Most likely Si and Sj are not a metameric pair to a human observer due to the different spectral sensitivities, though. It is obvious, that although this method gives acceptable results when e.g. samples of the Munsell Book of Colors are used for the training set and the validation set, it will normally fail on measuring arbitrary scenes due to metamerism. 7.5.3 Simulating the CCD Sensor The method presented in section 7.4 tries to find a mapping that converts light spectra to RGB values. No knowledge about the internal properties of the camera is needed. If this knowledge is available though, the following method may give 86
etter results: One can measure the spectral sensitivity curves of the camera using e.g. a monochromatic light source and a spectroradiometer. Then, instead of multiply- ing the spectrum of each pixel of the rendering image with the CIE color match- ing functions to get XYZ values, one can multiply it with the spectral sensitivity curves of the camera to get RGB values. In doing so, one avoids the most severe problem in using a camera for color validation, that is the different sensitivity curves of camera and human observer. We could not test this method as we do not have the required instruments to measure the spectral sensitivity curves of our camera. 87
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DISSERTATION Usability of Digital C
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Kurzfassung Physically Based Render
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Acknowledgements I want to thank We
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3 Colorimetry 32 3.1 Color Spaces .
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E.5 Treating the Camera as a Black
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20 The CIE 1931 chromaticity diagra
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53 (a) Density plot for using the c
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List of Tables 1 Camera settings th
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1 Introduction In recent years, a l
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1.3 Applications of Physically Base
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matter are calculated in XYZ space.
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Figure 2: A photograph of the real
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(a) Photo (b) Default (c) 2 Bounces
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y a light source on top of the cube
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Figure 7: Points A to F were select
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Figure 9: A difference image of pho
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Figure 12: From top to bottom: a hi
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(a) Figure 13: (a) Left side: measu
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Figure 14: Six photocells were posi
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Figure 16: The left image shows a r
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(a) (b) Figure 18: (a) The validati
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shape of the caustic. 2.3 Analytica
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two test cases, but instead of the
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segments of the sphere and its actu
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of luminance [GJB80]. In XYZ color
Page 51 and 52: where L ∗ � � Y m = 903.3 Yn
Page 53 and 54: (a) (b) Figure 21: Chromaticity dia
Page 55 and 56: 4 Digital Cameras Pattanaik et al.
Page 57 and 58: Figure 23: The RAW image data is co
Page 59 and 60: 5 Physically Based Rendering System
Page 61 and 62: see section 7.4. By avoiding XYZ sp
Page 63 and 64: Figure 27: The sensor plane marking
Page 65 and 66: (a) (b) Figure 30: (a) ColorChecker
Page 67 and 68: available on MAC systems. We chose
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Page 71 and 72: Figure 35: Eighty measurement spots
Page 73 and 74: (a) (b) (c) (d) Figure 37: (a) Lumi
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Page 77 and 78: i \ j 0 1 2 3 4 5 6 7 8 9 10 11 12
Page 79 and 80: (a) (b) (c) (d) Figure 43: (a) Lumi
Page 81 and 82: (a) (b) Figure 46: Standard deviati
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Page 85 and 86: of each patch using a spectrophotom
Page 87 and 88: Figure 51: L ∗ a ∗ b ∗ differ
Page 89 and 90: (a) (b) Figure 53: (a) Density plot
Page 91 and 92: (a) (b) (c) Figure 55: Histograms o
Page 93 and 94: Using an ICC profile obviously impr
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Page 99 and 100: in the light transport simulation o
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Page 105 and 106: Figure 62: A Mathematica generated
Page 107 and 108: their internal color space. This mi
Page 109 and 110: Figure 66: Surface reflectance valu
Page 111 and 112: as bad compared to other methods. S
Page 113 and 114: in sufficient detail. The approach
Page 115 and 116: A Radiometric and Photometric Quant
Page 117 and 118: B.6 Goniophotometer A goniophotomet
Page 119 and 120: C.3 Color Chart Figure 68: A Konica
Page 121 and 122: D Web Resources of Validation Data
Page 123 and 124: 8 bit jpg 16 bit 29 11.58 11.52 11.
Page 127 and 128: E.2 Using an ICC Profile 8 bit jpg
Page 131 and 132: 8 bit jpg 16 bit 124 11.08 10.89 11
Page 133 and 134: 1 2 3 4 31 9.10 3.61 4.26 5.96 32 8
Page 135 and 136: 1 2 3 4 93 6.25 4.11 4.05 8.69 94 4
Page 137 and 138: E.4 Adaptive Mapping - Higher Order
Page 139 and 140: 5 6 7 8 9 10 11 62 3.42 2.52 2.51 2
Page 141 and 142: 5 6 7 8 9 10 11 124 2.10 2.14 2.96
Page 143 and 144: R G B 31 -38.94 -25.49 29.26 32 -9.
Page 145 and 146: R G B 93 103.86 109.28 55.06 94 31.
Page 147 and 148: References [3dS06] 3D Studio Max: w
Page 149 and 150: [HLR00] Guowei Hong, M. Ronnier Luo
Page 151 and 152: Color Imaging Conference, pages 250
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[VMKK00] V. Volevich, K. Myszkowski
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