1. The document discusses the use of targets to measure and monitor imaging performance in cultural heritage and scientific imaging. Targets act as physical references that allow measurement of factors like resolution, grayscale response, and white balance.
2. Examples of targets that could be used include resolution targets, grayscale patches, and color targets. The document provides examples of how targets can be used to check for proper exposure, white balance, and resolution.
3. Using targets allows imaging workflows and equipment to be benchmarked and monitored over time to help ensure consistent quality and identify any issues that need to be addressed. The document provides tips on how to start implementing the use of targets in a pilot project.
Targets as Tools for Measuring Digital Image Quality
1. Image
Science
Associates
Targets as Tools
Not Talismans
Don Williams
- Image Science Associates d.williams@imagescienceassociates.com
If you aim at nothing, you’ll hit it every time
ZA – October, 2013
2. Image
Science
Associates
What makes cultural heritage
imaging different ?
- Future Re-purposing
- Variety of use cases
- Information durability
- Information critical
- Images, not pictures
- Scientific and
research component
- High volume, High speed
- Think manufacturing
- Think error management
ZA – October, 2013
4. Image
Science
Associates
Target Examples
• Intended for both calibration, performance
testing, and quality control.
• Act as an archeological reference for light and
resolution values. Required for change
detection.
• Things to consider:
- Do they suit your purpose ?
- How comprehensive are they ?
- Are they self described ?
- Reflection or Transmission
ZA – October, 2013
5. Image
Science
Associates
Why Use Targets ?
• Quality control and industry compliance
• Consistent product
• Verify vendor’s claims
• Managing expectations – acceptance testing
• Accurate Metadata population
• Diagnostics – problem solving and image
processing identification. Less rework
• Effective communication
• Corrective actions
Used by both
US and Netherlands Initiatives
ZA – October, 2013
6. Image
Science
Associates
The Top Ten FourTips
1 – Targets as Tools, Not Talismans
2 – Get the exposure correct
3 – Keep the Neutrals neutral
4 – Don’t confuse Pixels with Resolution
5–
6–
7–
8–
9–
10 –
ZA – October, 2013
7. Image
Science
Associates
What You’ll see
• Standardized ways to measure scanner/camera
performance
• Identify sources of variability in digital imaging
• Introduce imaging quality control procedures into
workflows.
• Use easy, non-disruptive ways to monitor
performance
ZA – October, 2013
8. Image
Science
Associates
What is an image ?
….and how is it measured ?
- A two dimensional spatial structure of varying light levels.
It is characterized by measuring a camera or
scanner’s response to light levels over a two
dimensional space. These levels are often
classified into colors types ( i.e. RGB)
- Changes in light can occur over short distances, like
edges,( high frequencies) or larger distances or areas,
like sky or facial features( low frequencies).
Targets act as known input references by which
a camera/scanner’s output can be compared.
SPACE & LIGHT
ZA – October, 2013
9. Image
Science
Associates
Digital Image Capture System
- From light to numbers -
The different ways an image is modified at capture
light
source
sample
Image
forming
optics
sensor
Processing
Digital image
file
ZA – October, 2013
1. Introduction
11. Image
Science
Associates
How Targets Are Used
to Measure and Manage Image Quality
variation
• Accuracy - Image values from a target
are compared with established aim
values. These values are typically usecase dependent.
• Precision - Tolerances around these
aims are also provided. Small tolerances
imply greater precision but also higher
production costs. The opposite is true for
large tolerances.
Consistent performance is often more
important than accuracy.
ZA – October, 2013
bias
12. Image
Science
Associates
- Targets –
Good measurements require good targets
Target Elements
1) ISO Frequency Response (SFR) and resolution
over the field of view. (7” x 10”)
2) Human interpretable resolution features
3) Dimensional scales for sampling confirmation
4) Automated feature detection
5) Neutral gray uniform background
6) Ten spectrally neutral gray patches
7) Self described colorimetric patch annotations
Device Target
Object Target
ZA – October, 2013
14. Correct Exposure via OECF ( tone transfer)
OECF example
250
average green count value ( 8 bit)
Image
Science
Associates
200
150
100
50
0
0
0.5
1
neutral (gray) density
Density
ZA – October, 2013
1.5
2
15. Image
Science
Associates
White Balance
- Keep all of the neutrals, neutral
OECFs can be measured for each color channel
using a target’s neutral gray patches.
85% of good color imaging performance is keeping
the Red, Green, and Blue OECFs the same… Really!
This is a good example of a well white balanced capture
Note that all color channel OECFs lie on top of each other
ZA – October, 2013
16. Image
Science
Associates
White Balance
This is an example where the white balance performance is marginal.
Note how the blue channel OECF departs from the red and green OECF
ZA – October, 2013
19. Image
Science
Associates
# Pixel quantity is not image resolution:
quantity vs. quality
Limiting resolution = whenever all five lines are undetectable
Resolution
Sampling
Frequency
Though the sampling rate is increased to 600 dpi the true resolution
for this scanner is only 300 dpi.
ZA – October, 2013
21. Image
Science
Associates
What’s missing from this picture ?
The different ways an image is modified at capture
light
source
sample
Image
forming
optics
sensor
Processing
Digital image
file
The performance of a digital capture system at the
sensor and beyond is influenced by all of the above in
addition to operator training and environment.
ZA – October, 2013
1. Introduction
28. Image
Science
Associates
How to Start
• Identify a small pilot project
– Equipment Benchmarking ?
– Workflow Monitoring ?
• Choose an appropriate target
– Device target
– Object target
• Practice introducing targets
– Will it remain in place ?
– Technique, not results
– How often ?
• Don’t do too much
– Do not analyze data
– Small amounts of data
• Collect data
ZA – October, 2013