High dynamic range imaging: acquisition, display, and image-based lighting
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Amsterdam [u.a.]
Elsevier [u.a.]
2010
|
| Edition: | 2. ed. |
| Series: | The Morgan Kaufmann series in computer graphics
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVIII, 650 S. Ill., graph. Darst. |
| ISBN: | 9780123749147 |
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MARC
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| 245 | 1 | 0 | |a High dynamic range imaging |b acquisition, display, and image-based lighting |c Erik Reinhard ... |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier [u.a.] |c 2010 | |
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Record in the Search Index
| _version_ | 1804143877976752128 |
|---|---|
| adam_text | Titel: High dynamic range imaging
Autor: Reinhard, Erik
Jahr: 2010
Contents
PREFACE TO THE SECOND EDITION XV
PREFACE TO THE FIRST EDITION XVII
01 INTRODUCTION 1
02 LIGHT AND COLOR 19
19
25
28
34
36
47
48
56
58
59
61
61
66
72
77
80
8?
LIGHT AND COLOR
2.1 Radiometry
2.2 Photometry
2.3 Colorimetry
2.4 Color Spaces
2.5 White Point and Illuminants
2.6 Spectral Sharpening
2.7 Color Opponent Spaces
2.7.1 CIELUV
2.7.2 CIELAB
2.7.3 IPT
2.8 Color Correction
2.9 Color Appearance
2.9.1 CIECAM02
2.9.2 Refinements
2.10 Display Gamma
2.11 Brightness Encoding
2.12 Standard RGB Color Spaces
CONTENTS
03 HIGH DYNAMIC RANGE IMAGE ENCODINGS 91
3.! Low-versus High Dynamic Range Encodings 91
3.2 Applications of HDR Images 93
3.3 HDR Image Formats 95
3.3. i The HDR Format 96
3.3.2 The TIFF Float and LogLuv Formats 98
3.3.3 The OpenEXR Format 10 3
3.3.4 Other Encodings 104
3.3.5 Lossy HDR Formats 105
3.3.6 Microsoft s HD Photo Format l 10
3.4 HDR Encoding Comparison I 10
3.5 Conclusions l 17
04 HIGH DYNAMIC RANGE VIDEO ENCODINGS 119
4.1 Custom HDR Video Coding l 23
4.1.1 Color Space for Lossy HDR Pixel Encoding l 23
4.1.2 MPEG-4 Extension to HDR Video I 30
4.1.3 An HDR Video Player l 3 3
4.2 Backwards-Compatible HDR Video Compression 136
4.2 I HDR MPEG - 4 Visual l 3 7
4.2.2 Scalable Bit-depth Coding in the H.264/AVC
Framework 141
05 HDR IMAGE CAPTURE 145
5 I Photography and Light Measurement 145
5.1.1 Camera RAW 147
5.2 HDR Image Capture from Multiple Exposures 148
5.3 Film Scanning I 51
5.4 Image Registration/Alignment 154
5.5 The Median Threshold Bitmap Alignment Technique I 5 S
5. S. I Threshold Noise 161
5.5.2 Overall Algorithm 16 4
CONTENTS
5.5.3 Efficiency Considerations 167
5.5.4 Results 168
5.5.5 Adding Rotational Alignment 16 9
5.6 Other Alignment Methods 170
5.7 Deriving the Camera Response Function 171
5.7.1 Debevec and Malik Technique 171
5.7.2 Mitsunaga and Nayar Technique 174
5.7.3 Choosing Image Samples for Response Recovery 177
5.7.4 Deriving Sample Values from Histograms 181
5.7.5 Caveats and Calibration 18 2
5.8 Noise Removal 183
5.9 Ghost Removal 185
5.10 Lens Flare Removal 18 8
5.10.1 The Point Spread Function 190
5.10.2 Estimating the PSF 193
5.10.3 Removing the PSF 196
5.11 HDR Capture Hardware 198
5.11.1 Capture of Multiple Exposures 198
5.11.2 Viper FilmStream 200
5.11.3 Panavision 2 01
5.11.4 Pixim 201
5.11.5 SpheronVR 202
5.11.6 Weiss AG 202
5.12Conclusion 203
06 DISPLAY DEVICES AND PRINTING
TECHNOLOGIES 205
6.1 Display Technologies 205
6.1.1 Cathode Ray Tubes 206
6.1.2 Plasma Displays 208
6.1.3 Liquid Crystal Displays 209
6.1.4 Reflective Display Technologies 213
CONTENTS
6.1.5 Projection Technologies 2 14
6.1.6 Emerging Display Technologies 215
6.2 Local-Dimming HDR Displays 216
6.2.1 Local-Dimming Television Screens 2 19
6.2.2 Dual Modulation in Large-Format Projection 220
6.2.3 Image Processing for Dual-Modulation Screens 2 2 1
6.3 Pnntmg 2 24
6.3.1 The Reflection Print 22 5
6.3.2 Transparent Media 227
6.3.3 Ward s HDR Still Image Viewer 2 2 8
6.4 Conclusions 2 30
07 PERCEPTION-BASED TONE REPRODUCTION 233
7.1 Tone-Mapping Problem 2 3.3
7.2 Human Visual Adaptation 237
7.2.1 The Pupil 239
7.2.2 The Rod and Cone Systems 240
7.2.3 Photopigment Depletion and Regeneration 243
7.2.4 Photoreceptor Mechanisms 243
7 3 Visual Adaptation Models for HDR Tone Mapping 2 52
7.3.1 Photoreceptor Adaptation Model for Tone
Mapping 252
7.3.2 The TV! Model for Tone Mappin g 25 5
7.4 Background Intensity m Complex Images 2 56
7.4.1 Image Average as /(, 2 56
7.4.2 Local Average as/(, 257
7.4.3 Multiscale Adaptation 260
7.5 Dynamics of Visual Adaptation 26 2
7.6 Design Considerations 264
7.6.1 Calibration 265
7.6.2 Color Images 268
7.6.3 Forward and Backward Models 270
7.7 Summary- 276
CONTENTS
08 TONE-REPRODUCTION OPERATORS 277
8.1 Sigmoidal Tone-Reproduction Operators 277
8.1.1 Photoreceptor Model 277
8.1.2 Photographic Tone Reproduction 285
8.1.3 Local Sigmoidal Compression 291
8.1.4 Adaptive Gain Control 294
8.2 Image Appearance Models 297
8.2.1 Multiscale Observer Model 297
8.2.2 iCAM 306
8.2.3 iCAM06 312
8.2.4 Color Appearance under Extended Luminance
Levels 31 7
8.3 Other HVS-Based Models 323
8.3.1 Brightness-Preserving Operator 324
8.3.2 Retinex 329
8.3.3 Ashikhmin s Model 331
8.3.4 Lightness Perception 338
8.3.5 Subband Encoding 340
8.3.6 Display-Adaptive Tone Reproduction 344
8.4 Apparent Contrast and Brightness Enhancement 349
8.4.1 Cornsweet Illusion 352
8.4.2 Glare Illusion 367
8.5 Other Tone-Reproduction Operators 376
8.5.1 Histogram Adjustment 377
8.5.2 Bilateral Filtering 380
8.5.3 Gradient Domain Compression 3 91
8.5.4 Multiscale Optimization Frameworks 396
8.6 Exposure Fusion 400
09 INVERSE TONE REPRODUCTION 405
9.1 Expansion Functions 406
9.1.1 Expanding Highlights 406
9.1.2 Inverse Photographic Operator 408
CONTENTS
9.1.3 Gamma Expansion 410
9.1.4 LDR2HDR 412
9.2 Under- and Overexposed Material 415
9.2.1 Hallucination 416
9.2.2 Video Processing 418
9.3 Suppressing Quantization and Encoding Artifacts 422
9.4 Preference Studies 42 5
9.4.1 Well-Exposed Images 425
9.4.2 Under-and Overexposed Images 428
9.5 Suggested Applications 432
9.6 Summary 434
10 VISIBLE DIFFERENCE PREDICTORS 435
10.1 Subjective versus Objective Quality Metrics 436
10.2 Classification of Objective Quality Metrics 438
10.3 FR Quality Metrics 438
10.4 Pixel-Based Metrics 440
10.4 I Perceptually Uniform Spaces for Pixel Intensity-
Encoding 442
10.4.2 JND-Scaled Space 444
10.5 SSIM Index 448
I 0.6 Perception-Based Fidelity Metrics 45 l
I0.6.I Multichannel Models 453
10 6.2 Multichannel HDR Image-Quality Metrics 454
10.7 The HDR Visible Differences Predictor 45S
10.7.1 Eye Optics Modeling 458
10.7.2 Amplitude Nonlinearity Modeling 460
10.7.3 Contrast Sensitivity Function 464
10.7.4 Cortex Transform 468
10.7.5 Visual Masking and Phase Uncertainty 472
10.7.6 Psychometric Function and Probability
Summation 477
CONTENTS
10.8 Dynamic Range-Independent Image-Quality Metric 479
10.8.1 Applications 483
10.8.2 Comparison with SSIM and HDR VDP 486
10.9 Suprathreshold HDR Image-Quality Metrics 487
10.9.1 Wilson s Transducer 489
10.9.2 Mantiuk s Transducer 491
10.9.3 A Metric Based on Wilson s Transducer 49 3
10.10 Accounting for Partial Adaptation 494
10.11 Summary 497
11 IMAGE-BASED LIGHTING 501
11.1 Basic IBL 504
11.1.1 Acquire and Assemble the Light Probe 504
11.1.2 Model the Geometry and Reflectance of the Scene 506
11.1.3 Map the Light Probe to an Emissive Surface
Surrounding the Scene 506
11.1.4 Render the Scene as Illuminated by the IBL
Environment 508
11.1.5 Postprocess the Renderings 508
11.2 Capturing light Probe Images 515
11.2.1 Photographing a Mirrored Sphere 5 16
11.2.2 Tiled Photographs 520
11.2.3 Fish-Eye Lenses 523
11.2.4 Scanning Panoramic Cameras 523
11.2.5 Capturing Environments with Very Bright Sources 525
11.3 Omnidirectional Image Mappings 53 5
11.3.1 Ideal Mirrored Sphere 535
11.3.2 Angular Map 537
11.3.3 Latitude-Longitude 538
11.3.4 Cube Map 540
11.4 How a Global Illumination Renderer Computes IBL Images 543
11.4.1 Sampling Other Surface Reflectance Types 547
CONTENTS
i 1.5 Sampling Incident Illumination Efficiently 549
1.5.1 Identifying Light Sources 55 5
1.5.2 Converting a Light Probe into a Constellation of
Light Sources 561
1.5.3 Importance Sampling 569
mutating Shadows and Scene-Object Intern-flection 57 2
1.6.1 Differential Rendering 576
1.6.2 Rendering into a Nondiffuse Local Scene 577
Useful IBL Approximations 579
1.7.1 Environment Mapping 579
1.72 Ambient Occlusion 583
mage-Based Lighting for Real Objects and People 586
1.8. i A Technique for Lighting Real Subjects 587
I 8 2 Relighting from Compressed Image Data Sets 588
1 1.9 Conclusions 593
! 1.6
1 1.7
APPENDIX A: LIST OF SYMBOLS
BIBLIOGRAPHY
INDEX
595
5S9
G41
|
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| discipline | Informatik |
| edition | 2. ed. |
| format | Book |
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| indexdate | 2024-07-09T22:54:39Z |
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| isbn | 9780123749147 |
| language | English |
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| physical | XVIII, 650 S. Ill., graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
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| publisher | Elsevier [u.a.] |
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| series2 | The Morgan Kaufmann series in computer graphics |
| spelling | High dynamic range imaging acquisition, display, and image-based lighting Erik Reinhard ... 2. ed. Amsterdam [u.a.] Elsevier [u.a.] 2010 XVIII, 650 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier The Morgan Kaufmann series in computer graphics Mustererkennung (DE-588)4040936-3 gnd rswk-swf Computervision (DE-588)4207417-4 gnd rswk-swf Computervision (DE-588)4207417-4 s Mustererkennung (DE-588)4040936-3 s DE-604 Reinhard, Erik 1968- Sonstige (DE-588)1063007127 oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021172434&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | High dynamic range imaging acquisition, display, and image-based lighting Mustererkennung (DE-588)4040936-3 gnd Computervision (DE-588)4207417-4 gnd |
| subject_GND | (DE-588)4040936-3 (DE-588)4207417-4 |
| title | High dynamic range imaging acquisition, display, and image-based lighting |
| title_auth | High dynamic range imaging acquisition, display, and image-based lighting |
| title_exact_search | High dynamic range imaging acquisition, display, and image-based lighting |
| title_full | High dynamic range imaging acquisition, display, and image-based lighting Erik Reinhard ... |
| title_fullStr | High dynamic range imaging acquisition, display, and image-based lighting Erik Reinhard ... |
| title_full_unstemmed | High dynamic range imaging acquisition, display, and image-based lighting Erik Reinhard ... |
| title_short | High dynamic range imaging |
| title_sort | high dynamic range imaging acquisition display and image based lighting |
| title_sub | acquisition, display, and image-based lighting |
| topic | Mustererkennung (DE-588)4040936-3 gnd Computervision (DE-588)4207417-4 gnd |
| topic_facet | Mustererkennung Computervision |
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