Usability of Digital Cameras for Verifying Physically Based ...

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8.1.1 RGB Rendering . . . . . . . . . . . . . . . . . . . . . . . 88 8.1.2 CIE XYZ Rendering . . . . . . . . . . . . . . . . . . . . 89 8.2 A Potential Problem with the Use of XYZ Color Space . . . . . . 90 8.3 Severity of the Problem in Practice . . . . . . . . . . . . . . . . . 91 9 Conclusions and Future Work 94 9.1 Using a Digital Camera for Verification Purposes . . . . . . . . . 94 9.2 Using XYZ Color Space as Internal Color Space . . . . . . . . . 95 9.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 A Radiometric and Photometric Quantities 99 B Measurement Devices - Definitions 100 B.1 Radiometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 B.2 Photometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 B.2.1 Luminance Meter . . . . . . . . . . . . . . . . . . . . . . 100 B.2.2 Illuminance Meter . . . . . . . . . . . . . . . . . . . . . 100 B.3 Spectroradiometer . . . . . . . . . . . . . . . . . . . . . . . . . . 100 B.4 Spectrophotometer . . . . . . . . . . . . . . . . . . . . . . . . . 100 B.5 Colorimeter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 B.6 Goniophotometer . . . . . . . . . . . . . . . . . . . . . . . . . . 101 B.7 Gonioreflectometer . . . . . . . . . . . . . . . . . . . . . . . . . 101 C Color Lab 102 C.1 Light Cabinet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 C.2 Luminance Meter . . . . . . . . . . . . . . . . . . . . . . . . . . 102 C.3 Color Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 C.4 Spectrophotometer . . . . . . . . . . . . . . . . . . . . . . . . . 103 C.5 Spectroscan T . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 D Web Resources of Validation Data 105 E Detailed Results 106 E.1 Using the Camera’s Color Space . . . . . . . . . . . . . . . . . . 106 E.2 Using an ICC Profile . . . . . . . . . . . . . . . . . . . . . . . . 111 E.3 Adaptive Mapping – Linear Polynomials . . . . . . . . . . . . . . 116 E.4 Adaptive Mapping – Higher Order Polynomials . . . . . . . . . . 121 vii
E.5 Treating the Camera as a Black Box . . . . . . . . . . . . . . . . 126 viii
Page 1 and 2: DISSERTATION Usability of Digital C
Page 3 and 4: Kurzfassung Physically Based Render
Page 5 and 6: Acknowledgements I want to thank We
Page 7: 3 Colorimetry 32 3.1 Color Spaces .
Page 11 and 12: 20 The CIE 1931 chromaticity diagra
Page 13 and 14: 53 (a) Density plot for using the c
Page 15 and 16: List of Tables 1 Camera settings th
Page 17 and 18: 1 Introduction In recent years, a l
Page 19 and 20: 1.3 Applications of Physically Base
Page 21 and 22: matter are calculated in XYZ space.
Page 23 and 24: Figure 2: A photograph of the real
Page 25 and 26: (a) Photo (b) Default (c) 2 Bounces
Page 27 and 28: y a light source on top of the cube
Page 29 and 30: Figure 7: Points A to F were select
Page 31 and 32: Figure 9: A difference image of pho
Page 33 and 34: Figure 12: From top to bottom: a hi
Page 35 and 36: (a) Figure 13: (a) Left side: measu
Page 37 and 38: Figure 14: Six photocells were posi
Page 39 and 40: Figure 16: The left image shows a r
Page 41 and 42: (a) (b) Figure 18: (a) The validati
Page 43 and 44: shape of the caustic. 2.3 Analytica
Page 45 and 46: two test cases, but instead of the
Page 47 and 48: segments of the sphere and its actu
Page 49 and 50: 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
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see section 7.4. By avoiding XYZ sp
Page 63 and 64:
Figure 27: The sensor plane marking
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(a) (b) Figure 30: (a) ColorChecker
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available on MAC systems. We chose
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(a) (b) (c) Figure 34: Standard dev
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Figure 35: Eighty measurement spots
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(a) (b) (c) (d) Figure 37: (a) Lumi
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(a) (b) Figure 40: (a) Luminance di
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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
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(a) (b) Figure 46: Standard deviati
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(a) (b) Figure 49: (a) The measurem
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of each patch using a spectrophotom
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Figure 51: L ∗ a ∗ b ∗ differ
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(a) (b) Figure 53: (a) Density plot
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(a) (b) (c) Figure 55: Histograms o
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Using an ICC profile obviously impr
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(a) (b) (c) (d) Figure 58: Histogra
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5 6 7 8 9 10 11 Min 0.50 0.49 0.13
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in the light transport simulation o
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the internal data of a camera and w
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etter results: One can measure the
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Figure 62: A Mathematica generated
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their internal color space. This mi
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Figure 66: Surface reflectance valu
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as bad compared to other methods. S
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in sufficient detail. The approach
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A Radiometric and Photometric Quant
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B.6 Goniophotometer A goniophotomet
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C.3 Color Chart Figure 68: A Konica
Page 121 and 122:
D Web Resources of Validation Data
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8 bit jpg 16 bit 29 11.58 11.52 11.
Page 125 and 126:
8 bit jpg 16 bit 91 17.21 17.11 17.
Page 127 and 128:
E.2 Using an ICC Profile 8 bit jpg
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8 bit jpg 16 bit 62 16.10 16.08 16.
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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
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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
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[HLR00] Guowei Hong, M. Ronnier Luo
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Color Imaging Conference, pages 250
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[VMKK00] V. Volevich, K. Myszkowski
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