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E-raamat: Camera Image Quality Benchmarking

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The essential guide to the entire process behind performing a complete characterization and benchmarking of cameras through image quality analysis

Camera Image Quality Benchmarking contains the basic information and approaches for the use of subjectively correlated image quality metrics and outlines a framework for camera benchmarking.  The authors show how to quantitatively compare image quality of cameras used for consumer photography. This book helps to fill a void in the literature by detailing the types of objective and subjective metrics that are fundamental to benchmarking still and video imaging devices. Specifically, the book provides an explanation of individual image quality attributes and how they manifest themselves to camera components and explores the key photographic still and video image quality metrics. The text also includes illustrative examples of benchmarking methods so that the practitioner can design a methodology appropriate to the photographic usage in consideration.

The authors outline the various techniques used to correlate the measurement results from the objective methods with subjective results. The text also contains a detailed description on how to set up an image quality characterization lab, with examples where the methodological benchmarking approach described has been implemented successfully. This vital resource:

  • Explains in detail the entire process behind performing a complete characterization and benchmarking of cameras through image quality analysis
  • Provides best practice measurement protocols and methodologies, so readers can develop and define their own camera benchmarking system to industry standards
  • Includes many photographic images and diagrammatical illustrations to clearly convey image quality concepts
  • Champions benchmarking approaches that value the importance of perceptually correlated image quality metrics 

Written for image scientists, engineers, or managers involved in image quality and evaluating camera performance, Camera Image Quality Benchmarking combines knowledge from many different engineering fields, correlating objective (perception-independent) image quality with subjective (perception-dependent) image quality metrics. 

About the Authors xv
Series Preface xvii
Preface xix
List of Abbreviations
xxiii
About the Companion Website xxvii
1 Introduction
1(26)
1.1 Image Content and Image Quality
2(16)
1.1.1 Color
3(5)
1.1.2 Shape
8(2)
1.1.3 Texture
10(1)
1.1.4 Depth
11(1)
1.1.5 Luminance Range
12(3)
1.1.6 Motion
15(3)
1.2 Benchmarking
18(4)
1.3 Book Content
22(5)
Summary of this
Chapter
24(1)
References
25(2)
2 Denning Image Quality
27(8)
2.1 What is Image Quality?
27(2)
2.2 Image Quality Attributes
29(2)
2.3 Subjective and Objective Image Quality Assessment
31(4)
Summary of this
Chapter
32(1)
References
33(2)
3 Image Quality Attributes
35(28)
3.1 Global Attributes
35(10)
3.1.1 Exposure, Tonal Reproduction, and Flare
35(4)
3.1.2 Color
39(1)
3.1.3 Geometrical Artifacts
40(1)
3.1.3.1 Perspective Distortion
40(2)
3.1.3.2 Optical Distortion
42(1)
3.1.3.3 Other Geometrical Artifacts
42(1)
3.1.4 Nonuniformities
43(2)
3.1.4.1 Luminance Shading
45(1)
3.1.4.2 Color Shading
45(1)
3.2 Local Attributes
45(11)
3.2.1 Sharpness and Resolution
45(4)
3.2.2 Noise
49(1)
3.2.3 Texture Rendition
50(1)
3.2.4 Color Fringing
50(1)
3.2.5 Image Defects
51(1)
3.2.6 Artifacts
51(1)
3.2.6.1 Aliasing and Demosaicing Artifacts
52(1)
3.2.6.2 Still Image Compression Artifacts
53(1)
3.2.6.3 Flicker
53(2)
3.2.6.4 HDR Processing Artifacts
55(1)
3.2.6.5 Lens Ghosting
55(1)
3.3 Video Quality Attributes
56(7)
3.3.1 Frame Rate
56(2)
3.3.2 Exposure and White Balance Responsiveness and Consistency
58(1)
3.3.3 Focus Adaption
58(1)
3.3.4 Audio-Visual Synchronization
58(1)
3.3.5 Video Compression Artifacts
59(1)
3.3.6 Temporal Noise
60(1)
3.3.7 Fixed Pattern Noise
60(1)
3.3.8 Mosquito Noise
60(1)
Summary of this
Chapter
60(1)
References
61(2)
4 The Camera
63(54)
4.1 The Pinhole Camera
63(1)
4.2 Lens
64(11)
4.2.1 Aberrations
64(1)
4.2.1.1 Third-Order Aberrations
65(1)
4.2.1.2 Chromatic Aberrations
66(1)
4.2.2 Optical Parameters
67(2)
4.2.3 Relative Illumination
69(1)
4.2.4 Depth of Field
70(1)
4.2.5 Diffraction
71(2)
4.2.6 Stray Light
73(1)
4.2.7 Image Quality Attributes Related to the Lens
74(1)
4.3 Image Sensor
75(16)
4.3.1 CCD Image Sensors
75(2)
4.3.2 CMOS Image Sensors
77(4)
4.3.3 Color Imaging
81(1)
4.3.4 Image Sensor Performance
82(7)
4.3.5 CCD versus CMOS
89(1)
4.3.6 Image Quality Attributes Related to the Image Sensor
90(1)
4.4 Image Signal Processor
91(11)
4.4.1 Image Processing
91(7)
4.4.2 Image Compression
98(1)
4.4.2.1 Chroma Subsampling
98(1)
4.4.2.2 Transform Coding
98(1)
4.4.2.3 Coefficient Quantization
99(1)
4.4.2.4 Coefficient Compression
100(1)
4.4.3 Control Algorithms
101(1)
4.4.4 Image Quality Attributes Related to the ISP
101(1)
4.5 Illumination
102(1)
4.5.1 LED Flash
103(1)
4.5.2 Xenon Flash
103(1)
4.6 Video Processing
103(8)
4.6.1 Video Stabilization
103(1)
4.6.1.1 Global Motion Models
104(1)
4.6.1.2 Global Motion Estimation
105(1)
4.6.1.3 Global Motion Compensation
106(1)
4.6.2 Video Compression
107(1)
4.6.2.1 Computation of Residuals
107(2)
4.6.2.2 Video Compression Standards and Codecs
109(1)
4.6.2.3 Some Significant Video Compression Standards
110(1)
4.6.2.4 A Note On Video Stream Structure
111(1)
4.7 System Considerations
111(6)
Summary of this
Chapter
112(1)
References
113(4)
5 Subjective Image Quality Assessment---Theory and Practice
117(50)
5.1 Psychophysics
118(2)
5.2 Measurement Scales
120(2)
5.3 Psychophysical Methodologies
122(4)
5.3.1 Rank Order
123(1)
5.3.2 Category Scaling
123(1)
5.3.3 Acceptability Scaling
124(1)
5.3.4 Anchored Scaling
125(1)
5.3.5 Forced-Choice Comparison
125(1)
5.3.6 Magnitude Estimation
125(1)
5.3.7 Methodology Comparison
126(1)
5.4 Cross-Modal Psychophysics
126(3)
5.4.1 Example Research
127(1)
5.4.2 Image Quality-Related Demonstration
128(1)
5.5 Thurstonian Scaling
129(2)
5.6 Quality Ruler
131(17)
5.6.1 Ruler Generation
134(1)
5.6.2 Quality Ruler Insights
135(1)
5.6.2.1 Lab Cross-Comparisons
135(1)
5.6.2.2 SQS2 JND Validation
136(3)
5.6.2.3 Quality Ruler Standard Deviation Trends
139(2)
5.6.2.4 Observer Impact
141(1)
5.6.3 Perspective from Academia
142(2)
5.6.4 Practical Example
144(3)
5.6.5 Quality Ruler Applications to Image Quality Benchmarking
147(1)
5.7 Subjective Video Quality
148(3)
5.7.1 Terminology
149(1)
5.7.2 Observer Selection
149(1)
5.7.3 Viewing Setup
150(1)
5.7 A Video Display and Playback
151(16)
5.7.5 Clip Selection
152(2)
5.7.6 Presentation Protocols
154(2)
5.7.7 Assessment Methods
156(2)
5.7.8 Interpreting Results
158(1)
5.7.9 ITU Recommendations
159(1)
5.7.9.1 The Double-Stimulus Impairment Scale Method
160(1)
5.7.9.2 The Double-Stimulus Continuous Quality Scale Method
160(1)
5.7.9.3 The Simultaneous Double-Stimulus for Continuous Evaluation Method
160(1)
5.7.9.4 The Absolute Category Rating Method
161(1)
5.7.9.5 The Single Stimulus Continuous Quality Evaluation Method
161(1)
5.7.9.6 The Subjective Assessment of Multimedia Video Quality Method
161(1)
5.7.9.7 ITU Methodology Comparison
162(1)
5.7.10 Other Sources
162(1)
Summary of this
Chapter
162(1)
References
163(4)
6 Objective Image Quality Assessment---Theory and Practice
167(60)
6.1 Exposure and Tone
168(2)
6.1.1 Exposure Index and ISO Sensitivity
168(1)
6.1.2 Opto-Electronic Conversion Function
169(1)
6.1.3 Practical Considerations
170(1)
6.2 Dynamic Range
170(1)
6.3 Color
171(10)
6.3.1 Light Sources
171(3)
6.3.2 Scene
174(2)
6.3.3 Observer
176(2)
6.3.4 Basic Color Metrics
178(2)
6.3.5 RGB Color Spaces
180(1)
6.3.6 Practical Considerations
181(1)
6.4 Shading
181(1)
6.4.1 Practical Considerations
182(1)
6.5 Geometric Distortion
182(2)
6.5.1 Practical Considerations
184(1)
6.6 Stray Light
184(1)
6.6.1 Practical Considerations
185(1)
6.7 Sharpness and Resolution
185(9)
6.7.1 The Modulation Transfer Function
186(5)
6.7.2 The Contrast Transfer Function
191(2)
6.7.3 Geometry in Optical Systems and the MTF
193(1)
6.7 A Sampling and Aliasing
194(10)
6.7.5 System MTF
195(3)
6.7.6 Measuring the MTF
198(1)
6.7.7 Edge SFR
198(3)
6.7.8 Sine Modulated Siemens Star SFR
201(2)
6.7.9 Comparing Edge SFR and Sine Modulated Siemens SFR
203(1)
6.7.10 Practical Considerations
204(1)
6.8 Texture Blur
204(3)
6.8.1 Chart Construction
206(1)
6.8.2 Practical Considerations
206(1)
6.8.3 Alternative Methods
207(1)
6.9 Noise
207(66)
6.9.1 Noise and Color
207(2)
6.9.2 Spatial Frequency Dependence
209(2)
6.9.3 Signal to Noise Measurements in Nonlinear Systems and Noise Component Analysis
211(1)
6.9.4 Practical Considerations
212(1)
6.10 Color Fringing
213(1)
6.11 Image Defects
214(1)
6.12 Video Quality Metrics
214(4)
6.12.1 Frame Rate and Frame Rate Consistency
215(1)
6.12.2 Frame Exposure Time and Consistency
215(1)
6.12.3 Auto White Balance Consistency
216(1)
6.12.4 Autofocusing Time and Stability
216(1)
6.12.5 Video Stabilization Performance
217(1)
6.12.6 Audio-Video Synchronization
218(1)
6.13 Related International Standards
218(4)
Summary of this
Chapter
221(1)
References
221(6)
7 Perceptually Correlated Image Quality Metrics
227(1)
7.1 Aspects of Human Vision
227(1)
7.1.1 Physiological Processes
227(5)
7.2 HVS Modeling
232(1)
7.3 Viewing Conditions
232(2)
7.4 Spatial Image Quality Metrics
234(10)
7.4.1 Sharpness
235(1)
7.4.1.1 Edge Acutance
235(2)
7.4.1.2 Mapping Acutance to JND Values
237(2)
7.4.1.3 Other Perceptual Sharpness Metrics
239(1)
7.4.2 Texture Blur
239(1)
7.4.3 Visual Noise
240(4)
7.5 Color
244(13)
7.5.1 Chromatic Adaptation Transformations
244(1)
7.5.2 Color Appearance Models
245(2)
7.5.3 Color and Spatial Content-Image Appearance Models
247(2)
7.5.4 Image Quality Benchmarking and Color
249(2)
7.6 Other Metrics
251
7.7 Combination of Metrics
252(1)
7.8 Full-Reference Digital Video Quality Metrics
252(21)
7.8.1 PSNR
253(3)
7.8.2 Structural Similarity (SSIM)
256(4)
7.8.3 VQM
260(2)
7.8.4 VDP
262(1)
7.8.4.1 Further Considerations
263(2)
7.8.5 Discussion
265(2)
Summary of this
Chapter
267(1)
References
267(6)
8 Measurement Protocols---Building Up a Lab
273(36)
8.1 Still Objective Measurements
273(20)
8.1.1 Lab Needs
274(1)
8.1.1.1 Lab Space
274(1)
8.1.1.2 Lighting
275(3)
8.1.1.3 Light Booths
278(1)
8.1.1.4 Transmissive Light Sources
279(1)
8.1.1.5 Additional Lighting Options
280(1)
8.1.1.6 Light Measurement Devices
281(1)
8.1.2 Charts
282(1)
8.1.2.1 Printing Technologies for Reflective Charts
282(4)
8.1.2.2 Technologies for Transmissive Charts
286(1)
8.1.2.3 Inhouse Printing
286(1)
8.1.2.4 Chart Alignment and Framing
287(2)
8.1.3 Camera Settings
289(1)
8.1.4 Supplemental Equipment
289(1)
8.1.4.1 Real World Objects
290(3)
8.2 Video Objective Measurements
293(4)
8.2.1 Visual Timer
293(1)
8.2.2 Motion
294(3)
8.3 Still Subjective Measurements
297(7)
8.3.1 Lab Needs
297(1)
8.3.2 Stimuli
298(1)
8.3.2.1 Stimuli Generation
298(3)
8.3.2.2 Stimuli Presentation
301(1)
8.3.3 Observer Needs
302(1)
8.3.3.1 Observer Selection and Screening
302(1)
8.3.3.2 Experimental Design and Duration
303(1)
8.4 Video Subjective Measurements
304(5)
Summary of this
Chapter
305(1)
References
305(4)
9 The Camera Benchmarking Process
309(44)
9.1 Objective Metrics for Benchmarking
309(2)
9.2 Subjective Methods for Benchmarking
311(4)
9.2.1 Photospace
312(1)
9.2.2 Use Cases
313(1)
9.2.3 Observer Impact
314(1)
9.3 Methods of Combining Metrics
315(2)
9.3.1 Weighted Combinations
316(1)
9.3.2 Minkowski Summation
316(1)
9.4 Benchmarking Systems
317(7)
9.4.1 GSM Arena
317(1)
9.4.2 FNAC
318(1)
9.4.3 VCX
318(1)
9.4.4 Skype Video Capture Specification
319(1)
9.4.5 VIQET
320(1)
9.4.6 DxOMark
321(2)
9.4.7 IEEE P1858
323(1)
9.5 Example Benchmark Results
324(21)
9.5.1 VIQET
324(1)
9.5.2 IEEE CPIQ
325(2)
9.5.2.1 CPIQ Objective Metrics
327(10)
9.5.2.2 CPIQ Quality Loss Predictions from Objective Metrics
337(1)
9.5.3 DxOMark Mobile
338(1)
9.5.4 Real-World Images
339(1)
9.5.5 High-End DSLR Objective Metrics
339(6)
9.6 Benchmarking Validation
345(8)
Summary of this
Chapter
348(1)
References
349(4)
10 Summary and Conclusions
353(6)
References
357(2)
Index 359
JONATHAN B. PHILLIPS, is a Staff Image Scientist at Google, USA. He is a United States delegate to the technical committee ISO/TC 42 Photography and a major contributor to the IEEE Camera Phone Image Quality (CPIQ) initiative.



HENRIK ELIASSON, PHD, is an image sensor and image analysis specialist at Eclipse Optics, Sweden. He is a senior member of SPIE.
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