Slide1 l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 65

Imaging Science for Archivists - 101 Don Williams - Image Science Associates - [email protected] ( Terms of Use ) PowerPoint PPT Presentation


Imaging Science for Archivists - 101 Don Williams - Image Science Associates - [email protected] ( Terms of Use ).

Download Presentation

Imaging Science for Archivists - 101 Don Williams - Image Science Associates - [email protected] ( Terms of Use )

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Slide1 l.jpg

Imaging Science for Archivists - 101Don Williams - Image Science Associates [email protected](Terms of Use)

Today’s standards for characterizing imaging performance and capability are based on an image science architecture. I begin by introducing this perspective and then describe its application to scanner and digital camera performance in an archiving environment. The standards and accompanying tools will help the understand tone and color response, resolution, and noise.

Anecdotal thinking comes naturally; Science requires training

Michael Shermer


Table of contents l.jpg

Table of Contents

  • What You’ll See

  • General Imaging Steps

  • Digital Capture Systems

  • Image Processing

  • Measurement Requirements

  • OECF

  • Spatial Resolution

  • SFR

  • Noise

  • Artifacts


Introduction background l.jpg

Introduction / Background


What you ll see l.jpg

Introduce Imaging Science Concepts

Signal and Noise

Quality, Performance, Capability

The Imaging Chain

Key imaging performance metrics

OECF

Spatial Frequency Response

Noise

Artifacts

Software execution, Results interpretations

…and corrective action ?

What you’ll see


Slide5 l.jpg

A number of these categories have ISO standards that define the metrology practice. Though intended for digital imaging devices their basis was derived from decades of analog (e.g., film) imaging experience.

Important Imaging Characteristics

Primary Imaging Performance Functions

• Signal – Any response that provides valued information

- Large area response to light

OECF -Opto-Electronic Conversion Function

- Spatial proximity behavior

Spatial Frequency Response – SFR ( or MTF)

• Noise – Any response that detracts from a desired signal

- Light intensity distortions – Total noise

- Geometric/Spatial distortions


What is an image and how is it characterized l.jpg

‘y’

‘x’

What is an Image… and how is it characterized ?

A two dimensional spatial structure of varying light levels and colors.

It is characterized by measuring physically realizable light intensities over a two dimensional space. These variations can occur over short distances, like edges, ( high frequencies) or larger distances or areas, like sky or facial features ( low frequencies).


The big picture general imaging steps l.jpg

Interpret or/and Display

input

output

Process

Acquire

CAPTURE

Encode - Converting light to numbers

DISPLAY

Decode - Converting numbers to light

The Big Picture- General Imaging Steps -

“The Digital Image”

An encoded proxy image

Imaging Performance Metrics indicate how an imaging system or component acts on, modifies or limits the effective optical characteristics of an input scene.

Once digitally captured, the image ceases to exist as light intensities. They are now encoded as N-bit ( M channel) digital files. Because the encoding is easily manipulated, ISO imaging capture performance metrics attempt to trace the data back to the original scene input intensities ( input referred ) or expected output intensities ( output referred )


What is a digital imaging system for capture l.jpg

Image forming

optics

Illumination

optics

detector

source

sample

Processing

What is a Digital Imaging System? ( for capture)

A collection of optical, software, or electronic functions that convert, encode, or otherwise act upon images or their optical or digital derivatives.

Digital image file

The performance of a digital capture system is influenced by all of the above in addition to operator training and environment.


How are the optical properties of objects characterized reflectance and transmission l.jpg

1000 units reflected

1000 units incident

= 1.000 reflectance

= 0.0 density

Incident

Illumination -

1000 units

100 units reflected

1000 units incident

= 0.1000 reflectance

= 1.0 density

10 units reflected

1000 units incident

= 0.0100 reflectance

= 2.0 density

1 unit reflected

1000 units incident

= 0.0010 reflectance

= 3.0 density

Reflective

or

transmissive

hardcopy

How are the optical properties of objects characterized ?- Reflectance and Transmission -

Density= - log10 (reflectance)

* In practice, it is often more convenient to specify in terms of density, especially for densities greater than 1.0


Know your collection typical density ranges for collection content l.jpg

Know your Collection- Typical density ranges for collection content -

B&W Photographic Paper

B&W Photographic Film

Kodachrome film

Color photographic paper

Q-13 target

Munsell papers

Motion picture print film

Non Photographic reflective material

Density


Know your collection targets object surrogates l.jpg

Know your CollectionTargets :object surrogates

• Intended for both calibration and performance testing

• Should be well characterized, and durable to light and handling.

• Got software ?

• How many if these colors are in your collection ?


Types of digital capture systems scanners vs cameras l.jpg

Fully Integrated – all capture components combined into a single plug-and-play scanner unit

Flatbed scanners – Epson ($), Creo IQSmart3 ($$$)

Copy Stand – Zeutschel, I2S products, Stokes

Special Purpose - Kirtas, Treventus, and Qidenus

D-I-Y Copy stands ( i.e. camera-on-a-stick)

Camera Backs – Betterlight, Hasselblad, Sinar,

Digital SLRs – Canon EOS, Nikon

Types of Digital Capture Systems- Scanners vs. Cameras -


Popular detector arrangements l.jpg

Popular Detector Arrangements

Linear arrays – one dimensional ordered arrangement of single detectors

Area arrays – two dimensional ordered arrangement of single detectors.

Step and Repeat (Macro or Micro) area strategy – capture and combine several area captures into a single large image


Linear arrays tri linear l.jpg

Technology -Detectors

Linear Arrays ( Tri-linear)

  • Three filtered ( Red, Green, Blue) rows of sensors

  • The sensor stares at the object in the row dimension and scans by the object in other direction. Sometimes called a pushbroom.

  • Used in scan back cameras ( e.g. BetterLight, PhaseOne FX, Seitz), flatbed scanners and most film scanners.

  • Frequently have different performance behaviors in the two directions different directions

From : The Focal encyclopedia of photography


2 d area arrays color filter array cfa pattern l.jpg

From : The Focal encyclopedia of photography

Technology -Detectors

2-D Area Arrays - Color Filter Array (CFA) pattern -

  • Three filter mosaic pattern ( Red, Green, Blue) of sensors. Sometimes other patterns are used but the “Bayer” pattern ( shown here) is by far the most popular. Used in virtually all consumer and professional digital cameras

  • One shot /one layer, sparsely populated color capture. Fully populated color achieved by interpolation algorithms (demosaicing) or micro-stepping.

  • Color filters integrated onto the sensor chip at manufacturing.

  • Occasionally will have subtle checkerboard artifacts or color aliasing rainbows in final delivered image.


Image processing the brains l.jpg

Image processing functions will absolutely affect imaging performance measurements. These include,

Image resizing, rotation

Unsharp masking and blurring sharpening

Tone/Color adjustments

Color profile changes

Noise reduction

Two approaches for performance measurement

Practice - measure the “as delivered” image file consistent with workflow practices.

Benchmarking – measure the raw image file with as many functions as possible set to “null”.

Image Processing- the brains -


Image performance vs imaging quality l.jpg

Imaging Performance: Objectively measured behaviors of an imaging system in preserving information of the original object.

For example: Tone response, Resolution, Noise, Color error, White balance, Light falloff ( uniformity )

Imaging Capability – Imaging Performance under optimal conditions

Operator

Environment

Ease of use

Both Accuracy and Precision are measurement requirements

Image Quality: Task, appearance, or use case dependent measure. It is almost always some weighted combination of imaging performance metrics.

Aerial reconnaissance – high resolution and low noise

Health Imaging – tone control, low noise, resolution is dependent on task

Document Imaging – OCR accuracy

Consumer Imaging – Memory-color saturation, moderate resolution

Sharpness

Graininess

Colorfulness

Naturalness

Image Performance vs. Imaging Quality


Slide18 l.jpg

X

X

X

X

X

X

X

X

X

X

Aim – The point or set of points intended to be hit

Measurement Requirements

Measurements usually require some level of both accuracy and precision.

  • Accuracy: average error from an aim

  • Precision: variability about the average reading

    Factors that influence measurements

  • Location on platen

  • Image processing

  • spatial sampling

  • image noise

  • environment

  • operator skill

Performance is more about consistency ( precision) than accuracy.

In imaging, accuracy is often not absolute but rather a preference.


N bits m channel eh l.jpg

N bits – M channel ?- eh ? -

Light intensities (i.e., analog signal) reaching the detector are assigned one of 2N digital count values for each of M color channels. For grayscale and high quality color imaging N= 14-16 bits and M=3 color (RGB) channels. This is referred to as N bit quantization

28 = 256 values ( 0-255) : 216 = 65536 values (0-65535)

High light intensity = high reflectances = low density  high count value

Low light intensity = low reflectances = high density  low count value


N bits m channel utility l.jpg

N bits – M channel ? - utility -

Raw image files are typically delivered as 16 bit/channel or a total of 48 bit color. These files are often maintained as digital or safety masters because they allow maximum future utility.

2(16*3) = 65536 levels for each of three colors. 48 bit color

More commonly, a scanner’s or camera’s software will use the higher number of raw bits to provide a “finished file” of lesser number of bits that is consistent with a user’s specific application. Future utility of this finished file is compromised though.

21 = 2 levels ( 0-1 ) one bit or bi-tonal image

28 = 256 levels ( 0-255 ) 8 bit or grayscale image

2 (8*3) = 256 levels for each of three colors. True or 24 bit color.


Slide21 l.jpg

- Image Speak -


Slide22 l.jpg

Acutance

Geometric Distortion

Lateral Color

Error

White

Balance

Wobble

Depth of

field

Sharpness

Delta E

Shading

Exposure

Gamma

Sharpening

Aliasing

Dynamic

Range

Noise

Flare

Exposure

Resolution

Signal

Noise

Vocabulary

- staying dry in a storm of vernacular idioms -


Slide23 l.jpg

Imaging Performance Frameworkhttp://www.digitizationguidelines.gov/stillimages/documents/imaging.html


Signal any response that provides valued information l.jpg

SIGNALAny response that provides valued information

ISO 22028-1


Signal any response that provides valued information25 l.jpg

SIGNALAny response that provides valued information


Noise any response that detracts from a desired signal l.jpg

NOISEAny response that detracts from a desired signal


Noise any response that detracts from a desired signal27 l.jpg

NOISEAny response that detracts from a desired signal


Slide28 l.jpg

Opto Electronic Conversion Function (OECF)ISO 14545akaTone Transfer CurveorTone Reproduction Curve (TRC)


How is the oecf created l.jpg

(1) Scan grayscale step target with known optical reflectances*

(2) Compute average count value from the digital image file of scanned grayscale target

(3) Table of reflectance – average count value data pairs for each color channel

average count value

average count value

neutral

optical

red

green

blue

red

green

blue

reflectance

8

8

7

13

13

14

0.010

8

8

7

18

17

18

0.013

13

13

14

24

25

25

0.016

18

17

18

31

32

30

0.020

24

25

25

38

40

39

0.025

31

32

30

45

46

45

0.033

38

40

39

52

51

51

0.042

45

46

45

60

62

61

69

70

69

0.051

52

51

51

81

82

81

0.065

60

62

61

92

93

92

0.083

69

70

69

105

105

104

0.107

81

82

81

119

120

120

0.132

92

93

92

135

132

132

0.166

105

105

104

153

155

154

0.209

119

120

120

172

173

172

0.263

135

132

132

194

193

193

219

220

220

0.339

153

155

154

247

250

249

0.437

172

173

172

0.550

194

193

193

0.708

219

220

220

0.912

247

250

249

How is the OECF created ?

* Most grayscale targets come with documentation on reflectances/densities of each patch. Over time, these may fade or change though. A densitometer can be used to verify any change.


Graphical representation of oecf l.jpg

Density

Reflectance

Graphical Representation of OECF

Using Density is more revealing since the data

values tend to be more evenly distributed


Standards guidelines exposure efficiently assigning density to count values l.jpg

Standards, Guidelines, Exposure- efficiently assigning density to count values -

If most measured data values are above a selected OECF aim curve, over-exposure is indicated, conversely

if most measured data values are below a selected OECF aim curve, under-exposure is indicated


How do software driver setting terms effect oecf l.jpg

How do software driver setting terms effect OECF ?

  • • Shadow and Highlight settings provide upper and lower boundaries for controlling the darkest lightest light values encoded in the digital file. Some of these behave very much like the “Levels” function in Photoshop by allowing control of the boundary values of the OECF

  • • Auto contrast features are usually proprietary algorithms that provide custom OECFs through internal gamma, shadow, and highlight settings.

  • Because they do this on an image-by-image basis, inconsistent OECF behaviors occur within acollection.

  • • A higher Gamma setting at capture will increase the brightness of a displayed image…….. do not assume that a given gamma setting will yield the same OECF behavior for all scanners or software drivers.


White balance neutrality keep all of the neutrals neutral l.jpg

White Balance / Neutrality-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


White balance neutrality keep all of the neutrals neutral34 l.jpg

White Balance / Neutrality-Keep all of the Neutrals neutral -

This is an example where the white balance performance is poor.

Note how the blue channel OECF departs from the red and green OECF


Slide35 l.jpg

White Balance Assessment using

Photoshop Histograms

Though gray patches appear gray, the one patch is actually slightly colored.

All three histogram should align if white balance is correct


A few words on color encoding error e erroneously called color accuracy l.jpg

A few words onColor Encoding Error ( ∆E)- erroneously called color accuracy -

• Digital cameras/ scanners do not reproduce colors;

they encode them.

• Color accuracy implies that two physically realizable

colors are at hand to compare.

• Assumptions on how RGB code values will ultimately be

interpreted ( i.e. rendered or decoded) as physically reproduced color

are often very wrong.

• Delta E ( i.e. color error ) measures for digital capture devices

are suitable for performance consistency monitoring but not as

absolute measures of color accuracy


Slide37 l.jpg

Example ∆E Analysis for Adobe RGB encoding

Summary measures for

color encoding error


Slide38 l.jpg

OECF – Summary and Recommendations

  • There is no single best or standardized OECF. Most are based on policy maker or user preferences for the “look” of the image with respect to a particular use case.

  • Generally, any gamma setting between 1.5 – 2.5 is fine for capturing all significant image information for common reflection media densities.

  • The important practice is to document what the OECF is for ease of future image reproduction and rendering.

  • 85% of good color management relies on keeping the neutrals neutral. Do not assume that individual color channel OECFs for a gray scale target will be identical. By documenting the OECF though, any mismatch can usually be corrected. ( called “corrective action” )

  • Leave sufficient CV ”buffer” in the OECF for future tone and spatial image manipulations. Dmax≈15 count value (CV), Dmin≈ 240 CV

  • An alternative to documenting the OECF is to include an image of a grayscale target in each digital image file and maintain it as part of the image file. Be sure the optical densities of each patch are somehow indicated.


Spatial resolution spatial frequency response sfr iso 16067 1 16067 2 12233 15529 l.jpg

Spatial ResolutionSpatial Frequency Response (SFR)ISO 16067-1, 16067-2, 12233, 15529


Slide40 l.jpg

Recall the context…


What is spatial resolution and what are all those related terms l.jpg

Related Metrics

  • • Number of pixels

  • Sampling frequency - dpi, ppi

  • *Limiting Resolution, Resolving Power

  • *Spatial Frequency Response - SFR

  • *Modulation Transfer Function - MTF

What is Spatial Resolution….and what are all those related terms ?

• The ability of an imaging component or system to distinguish finely spaced detail. Specifically, the ability to maintain the relative contrast of finely spaced detail.

• Highest frequency ( smallest distance) at which light and

dark parts of image are reliably distinguishable. Sometimes called limiting resolution

*Related Standards – ISO 16067-1, 16067-2, 12233, & 15529 all employ these metrics


Space and frequency l.jpg

Two ways to look at image details

Space: size of the smallest important (signal) feature (mm)

Frequency: How many small important features/area will my image store (number/mm, cycles/mm, dots/inch, pixels/inch)

Small size implies high frequency

Why usespace vs. frequencydescriptions?

Compatible with engineering descriptions of information, bandwidth

Simplifies some forms of system analysis

Compatible with several visual image quality descriptions

Space and Frequency


Spatial resolution l.jpg

 Two factors in a digital imaging system dictate the level of spatial resolution or detail that can ultimately be captured.

Quantity; sampling frequency ( i.e. samples per mm, inch )

Sets a capability limit that can be achieved

Quality; effective optical quality

Defines the level of optical “blur” that the imaging optics, environmental factors, hardware, and image processing impose on the captured image.

These are necessary but insufficient components, by themselves, for capturing spatial detail. Simply improving one to reconcile deficiencies in the other will not enhance real resolution performance.

Spatial Resolution


Sampling frequency spot size and resolution l.jpg

peak-to-peak difference

peak-to-peak sum

= 0.10

≡ modulation =

Clearly resolved

1.0 - 0.81

1.0 + 0.81

Just resolved

Not resolved

Sampling Frequency…Spot Size…and Resolution

Cross section Illustration of two spots just resolved according to the Rayleigh criterion

(c. 1879)


Limiting resolution so how does one measure it l.jpg

Traditional method using bar targets and visual evaluation

Imaged target

P

P

P

P

P

P

P

P

P

P

Yes, the features

are readable

Limiting Resolution=

That spatial frequency at which finely spaced features are no longer visually detectable

P

P

Ð

P

P

Ð

Ð

P

P

P

P

P

P

P

No, the features

are illegible

P

P

P

Ð

Ð

Ð

Increasing spatial frequency

( lp/mm or cycles/mm)

CONS

* Relies on human readable subjective evaluation

* Results are target contrast dependent

* Provides little diagnostic or image quality insight

* Purely an ON/OFF (threshold) criterion

PROS

* Simple to measure

* Easily understood

* Suitable for binary scanners

- Limiting Resolution -….so how does one measure it ?


Sampling frequency is not resolution l.jpg

Sampling Frequency is not Resolution

Increasing # lines/inch

DPI (PPI) -> Quantity of data

Resolution -> Quality of data (information)

(line width = 1/200 “)

(line width = 1/300 “)

(line width = 1/400 “)

Resolution Wedge Test Feature Example


Slide47 l.jpg

Sampling rate (DPI) is not Resolutionreal data example : results are not simulated

Resolution limit = whenever all five lines are undetectable

Resolution

Sampling

Rate

Though the sampling rate is increased to 600 dpi the true resolution for this scanner is only 300 dpi.


Spatial frequency response sfr the better alternative l.jpg

Target feature modulation after being imaged

Modulation Transfer =

Target feature modulation before being imaged

Limiting resolution will generally

correspond to the spatial frequency having a 0.10 modulation transfer value

P

P

P

P

P

P

P

P

P

P

1.0

Modulation Transfer

0.5

0.0

Ð

Ð

Ð

Increasing spatial frequency

( lp/mm or cycles/mm)

–Spatial Frequency Response* (SFR) the better alternative

Instead of using a subjective YES/NO criterion along the vertical axis, an objective contrast (i.e. modulation) transfer ratio is adopted. The SFR it is an indicator of modulation( i.e. contrast) loss as a function of spatial frequency.

* Also referred to as the Modulation Transfer Function (MTF)


How limiting resolution fails to predict quality why the sfr is better for doing so l.jpg

How limiting resolution fails to predict quality& why the SFR is better for doing so.


Slide50 l.jpg

Anatomy of the SFR- regions of behavior -

Sharpening operators

(over-sharpening, halo-ing, scream)

Aliasing artifacts

( jaggies, moire)

Limiting resolution

Flare (haze)

Acutance and Sharpness


Slide51 l.jpg

Sharpness vs. Resolution


How is the sfr measured l.jpg

How is the SFR Measured ?

...all you need is an image of a GOOD slanted edge.


Slide53 l.jpg

Max. measurable frequency

at 600 dpi sampling

fast

slow

Flatbed scanner using a linear array

Horizontal (fast) and Vertical (slow) scan directions


Sfr in review l.jpg

What is it?

descriptive plot that measures the extent to which image detail contrast (modulation) is maintained by an imaging component or system.

What is a spread function?

image resulting from a point, or very narrow line exposure.

What it is not?

same as ‘resolution’

sampling frequency or number of pixels

complete descriptor of image quality (noise, color, …)

What good is it?

It provides a way to analyze the influence of imaging components on the retention and reproduction of low, medium, and high spatial frequencies ( flare, sharpening, limiting resolution )

Which digital imaging standards address it ?

ISO 16067-1: Resolution for digital print scanners

ISO 16067-2: Resolution for digital film scanners

ISO 12233 : Resolution for digital still cameras

ISO 15529 : MTF for sampled imaging systems

- SFR in Review -


Noise random and otherwise l.jpg

NoiseRandom and otherwise


Scanner noise characterization l.jpg

Scanner Noise Characterization

OECF data

RMS Noise data


Scanner noise characterization57 l.jpg

Scanner Noise Characterization


Two forms of random noise l.jpg

Course-grain noise ->

low frequency fluctuations

Fine-grain noise ->

high frequency fluctuations

Two Forms of Random Noise

Both of these images have the same amount of total noise


One dimensional random noise banding and streaking l.jpg

One-dimensional random noise- banding and streaking -

• Often found on devices using linear arrays

• Contrast and equalize enhancements are

useful in detecting such noise behaviors

Following contrast enhancement


Artifacts l.jpg

Odd imaging behaviors that seem to defy objective measurement…for the time being.

De-texturing

Checkerboarding

Localized geometric distortions

Sensor defects

Color aliasing

Many are technology specific. Just knowing what type of scanner technology was used allows one to prepare for certain artifacts

Artifacts


Slide61 l.jpg

Visual Inspection for Artifacts

Color Misregistration


Slide62 l.jpg

resolution limit due to CFA de-mosaicing

optical resolution limit

Visual Inspection for Artifacts

Visual assessments of spatial artifacts is quite reliable


Slide63 l.jpg

Visual Inspection for Artifacts

Visual assessments of spatial artifacts is quite reliable

Bad Cam in a Linear array scanner


Workflow performance monitoring oecf l.jpg

Workflow Performance Monitoring - OECF

Book Scanner Example


Slide65 l.jpg

End

1907 autochrome


  • Login