1. EE 638: Principles of Digital Color Imaging Systems
Lecture xx: Image Quality

2. Image quality – an illustrative example
Prof. Charles A. Bouman is honored for his service as Editor of the IEEE Transactions on Image Processing (shown with Prof. Ali H. Sayed, UCLA, VP-Conferences and Prof. Jose M. F. Moura, CMU, President, IEEE SP Society)
Open image in Photoshop to be sure about what we are seeing.
What are the image quality defects that are visible here?
”It's a little frightening to think that this picture is associated with the Transactions on Image Processing” – C. A. Bouman

3. Image quality defects present in image
Noise in low-light parts of scene
Moire on Prof. Bouman’s shirt
JPEG artifacts
Poor color rendering – too red
Poor contrast and tone – too dark
Glare from flash

4. Overview
As imaging continues to become more pervasive, image quality has become a topic of growing interest.
We have done a great deal of work in this area over the years within our laboratory. I will use this work as a partial basis for the discussion in class.
These will be augmented by materials from other sources.

5. Image quality resources – textbooks
Brian W. Keelan, Handbook of Image Quality Characterization and Prediction, Marcel Dekker, 2002 (Kodak’s “go-to guy” for image quality).
Peter Engeldrum, Psychometric Scaling: A Toolkit for Imaging Systems Development, Imcotek Press, 2000.

6. Image quality resources – journals
IEEE Transactions on Image Processing
Journal of Electronic Imaging (IS&T and SPIE)
Journal of Imaging Science and Technology (IS&T)

7. Image quality resources – conferences
IEEE International Conference on Image Processing (ICIP)
IS&T/SPIE Electronic Imaging
Image Quality and System Performance
Human Vision and Electronic Imaging
Color Imaging: Displaying, Processing, Hardcopy, and Applications
Digital Photography
Others…
IS&T International Conference on Digital Printing Technologies (NIP) (The conference has shifted focus to functional printing.)
IS&T Color Imaging Conference

8. Image quality resources – standards
ISO/IEC Information technology – Office equipment – Measurement of image quality attributes for hardcopy output – Binary monochrome text and graphic images. (

9. Image quality – role of human viewer
Image quality is all about what humans see
Pyschophysics is therefore an essential tool for understanding image quality

10. Three image quality questions (refer to photo of Prof. Bouman)
Is image quality impairment visible?
Is image quality impairment acceptable?
Among different choices for a particular component of the imaging system that impact this particular image quality impairment, which do I prefer?
The answer to Question 1 depends on viewing conditions.
The answer to Questions 2 and 3 depends on intended application, market segment, and viewer characteristics.

11. Imaging Pipeline Image output Image processing Image capture Enhance
Compose
Compress
Camera
scanner
Display
Printer

12. Image quality perspectives – image vs. system
Imaging systems based
Resolution (modulation transfer function)
Dynamic range
Noise characteristics
Image-based
Sharpness
Contrast
Graininess/mottle

13. Image quality vs. print quality
Broader viewpoint
Often focuses on issues that arise during Image processing phase below, especially compression.
May also consider image capture and display
Print quality
Specifically considers issues that arise during printing
Image capture
Image processing
Image output

14. Typical image quality issues
See discussion of photo of Prof. Bouman

15. Typical print quality issues
Bands – orthogonal to process direction
Streaks – parallel to process direction
Spots
Repetitive
Random
Color plane registration errors
Ghosting
Toner scatter
Swath misalignment

16. Image quality assessment functionalities
Metrics vs. maps
Local or global strength of a particular defect – a single number
Map showing defect strength throughout the image – an image
Single defect vs. summative measures
Assess strength of a single defect, i.e. noise
Assess overall image quality – must account for all significant defects and their interactions
Reference vs. no-reference methods

17. Image quality assessment factors
Masking – image content may mask visibility of defect
Texture
Edges
Tent-pole effect – worse defect dominates percept of image quality defects and overall assessment of image quality

18. General measures for imaging systems quality
Based on use of specific input targets
Point-and-shoot camera – Walmart photoprocessor – desktop scanner imaging pipeline (Sangho Kim and Buyue Zhang)
Modulation transfer function (MTF)
System noise (primarily film grain noise)
Application – optimal unsharp mask and adaptive bilateral filter
Inkjet printer (Woon-Young Jang)
Complicated by unique characteristics of halftoning process
Application – pre-enhancement of digital photographs before printing to compensate for roll-off in printer MTF

19. General measures for image quality
Sharpness metrics and pyschophysical assessment (Buyue Zhang)
No-reference method
Image fidelity assessor (Chris Taylor and Wen Wu)
Reference-based
Explicitly models processing of HVS
Produces visible differences map
Application – no specific target application
Formatter qualification (Du-Yong Ng)
Targeted toward certain classes of artifacts, but generally applicable
Application – firmware qualification for printer development

20. Measuring imaging systems artifacts – banding
Comparison between different printers (10-printer experiment) (Yousun Bang)
Softcopy environment for monochrome banding assessment (Osman Arslan)
Absolute threshold for banding
Tool for banding analysis (Thanh Ha)
Softcopy environment for color banding assessment (Byungseok Min)
Discrimination threshold for color banding
Absolute threshold for color banding
Relative roles of lightness, hue, and saturation in banding visibility

21. Measuring imaging systems artifacts – quality of lines and edges
Toner scatter (Hyung Jun Park)
Inkjet swath misalignment (Edgar Bernal)

22. Measuring image artifacts
JPEG ringing artifacts (Xiaojun Feng and Sirui Hu)
Moire artifacts (Mu Qiao)
Image rescaling artifacts (Ari Suwendi)