Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Light
- 3 Radiometry
- 4 Photometry
- 5 Light–matter interaction
- 6 Colorimetry
- 7 Light sources
- 8 Scene physics
- 9 Optical image formation
- 10 Lens aberrations and image irradiance
- 11 Eye optics
- 12 From retina to brain
- 13 Visual psychophysics
- 14 Color order systems
- 15 Color measurement
- 16 Device calibration
- 17 Tone reproduction
- 18 Color reproduction
- 19 Color image acquisition
- 20 Color image display
- 21 Image quality
- 22 Basic concepts in color image processing
- Appendix Extended tables
- Glossary
- References
- Index
21 - Image quality
Published online by Cambridge University Press: 16 January 2010
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Light
- 3 Radiometry
- 4 Photometry
- 5 Light–matter interaction
- 6 Colorimetry
- 7 Light sources
- 8 Scene physics
- 9 Optical image formation
- 10 Lens aberrations and image irradiance
- 11 Eye optics
- 12 From retina to brain
- 13 Visual psychophysics
- 14 Color order systems
- 15 Color measurement
- 16 Device calibration
- 17 Tone reproduction
- 18 Color reproduction
- 19 Color image acquisition
- 20 Color image display
- 21 Image quality
- 22 Basic concepts in color image processing
- Appendix Extended tables
- Glossary
- References
- Index
Summary
The performance of a color imaging system is often evaluated by the image quality it can deliver to the user. Image quality can be evaluated physically (objective image quality) or psychophysically (subjective or perceptual image quality) or both. In this chapter, we will discuss some of the metrics and procedures that are used in image quality measurements. Objective image quality measures, such as resolving power, noise power spectrum, detective quantum efficiency (DQE), and system MTF, are well defined and can often be measured consistently [64]. However, they may not be directly correlated with the perceived image quality. Therefore psychophysical procedures are used to construct metrics that relate to the subjective image quality. Given our inadequate understanding of image perception, one may even argue that the definitive quality evaluation can only be done by human observers looking at images and making judgments. Therefore, the subjective quality rating is the only reliable metric for image quality. Although this statement is true, it does not help us much in developing better imaging systems because human judgment is too time-consuming, costly, and not always consistent. Objective image quality metrics are needed for many product optimizations and simulations.
In the past (before 1970), image quality was often measured on a system level. With the advance and availability of digital imaging devices, quality metrics for individual digital images have also been developed. These image-dependent image quality measures are becoming more and more important because they can be used to detect and correct problems before images are displayed or printed. An automatic correction algorithm for individual images requires a reliable image quality metric that can be computed from a digital image [489, 672].
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- Chapter
- Information
- Introduction to Color Imaging Science , pp. 564 - 584Publisher: Cambridge University PressPrint publication year: 2005