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Author(s): Paul Conway, 2008-2011. License: Unless otherwise noted, this material is made available under the terms of the Creative Commons Creative Commons Attribution - Non-Commercial - Share Alike 3.0 License: http://creativecommons.org/licenses/by-nc-sa/3.0/
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SI 675 Digitization for Preservation
Week 4 – Color in Scanning and Scanner Benchmarking
Today’s Outline
Encoding Performance measurement
Spatial Frequency Response Tone and color response Noise and artifacts
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“Original-referred images maintain a defined relationship between the original and digital version; the digital version carries forward certain pre-identified essential characteristics of the original version.” (p. 9)
• Puglia/Rhodes, Digitization for Preservation (2007)
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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).
‘x’
‘y’
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What is a Digital Imaging System?
A collection of optical, software, or electronic functions that convert, encode, or otherwise act upon images or their optical or digital derivatives.
Processing
source
Illumination optics
Image forming optics
detector
sample
The performance of a digital capture system is influenced by all of the above in addition to operator training and environment.
Digital image file
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Rendering Intents Revisited
When “… digital image is a ‘replacement’ of the original…” (p. 28) Decisions made about color space before images are
digitized. Adjustments made during scanning. “… not be the goal to reproduce the physical properties
of the original, but to reproduce its appearance under certain viewing conditions.”
Scanned image matches original (viewing calibration) Adjust for errors in the source Original appearance rendered (reverse aging) “Original scene” (remove effects of intermediate)
• Frey & Reilly, Digital Imaging, 1999 $ 2006
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Master Files: Raw or Cooked
“One of the basic things to understand is that a great deal of image quality can be lost by processing the images at the moment they are captured.” Frey & Reilly (1999): First, a so-called archive file containing more
than eight bits per channel should be stored. It should not be treated for any specific output.
Frey & Chapman (2000): save unrendered image space to contain a colorimetric estimate of the original. (p. 168)
Susstrunk (2002): capture and encode into a sensor or source space, which is device and image specific. (p. 2)
Susstrunk (2002): transform from raw sensor data (device dependent) to original-referred color encoding; then image dependent processing to output-referred color encoding to output device coordinates. (p. 389)
Puglia/Rhodes (2007): Original-referred is not raw (sensor)
• Frey & Chapman, Developing Specifications, 2000.
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Definitions and Terms
Original vs. scene: scanner/camera OR artifact/nature Color encoding: relationship of the real and its representation Color model: abstract mathematical model of color
representation (e.g. RGB, CMYK) Gamut: subset of colors that can be accurately represented in a
given circumstance (e.g. color space or output device). Color spaces: color model + gamut Absolute color space: colors are unabiguous; interpretation is
colorimetrically defined without reference to external factors. ICC color profile: color attributes of a particular device or
viewing requirement ; map between color space and profile connection space (CIELAB or CIEXYZ)
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Digital Cameras do not Reproduce Colors, They Encode them.
They describe (encode) object color characteristics. Reproduction requires interpreting (decoding) the pixel values to display the
color image.
Source Object
Color Negative Densities ( Encoded)
Capture Render
Color film negative path
Source Object
Encoded Count Values
Printed or display
Capture Render
Color digital camera path
Digital negative?
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Color Encoding Flowchart • Susstrunk, Color Encodings (2002).
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Raw Files, Be Careful What You Ask For
De-mosaicing Raw color interpolated Data
Original Scene or object Raw corrected
CFA Data Sensor Sensor defect, uniformity, correction
Scene referred Color encoding
white balance, camera color correction
Display transform
Softcopy image, or print
Color rendering and output referred encoding
Output referred Color encoding
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Sensor Encoding
“When a high quality master file is desired, storing ‘raw image’ data encoded in sensor space is most appropriate. Any further processing can degrade the image, as algorithms are based on today’s know-how and might be improved in the future.” (p. 390)
• Susstrunk, Managing Color (2002).
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Input- and Output-Referred Encoding Input-Referred Encoding (master)
Scene versus Original Photo YCC (Kodak Photo CD) RIMM RGB ICC PCS
Output-Referred Encoding (derivative)
Transformed from source or input sRGB (screen and color printing) E-sRGB (rendering for unknown output) ROMM RGB (extended bit depth) Adobe RGB (graphic arts and printing)
• Susstrunk, Color Encodings (2002).
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Attributes of color encodings • Susstrunk, Color Encodings (2002).
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Color Gamut Comparison
Range of Chromaticities Typical CRT Gamut
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[Susstrunk 2007]
Color Space Comparison
Shape of Visible Color Gamut Comparisons
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[Susstrunk 2007]
Color Space Comparison
Adobe RGB Color Space ROMM (ProPhoto) RGB
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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
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
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Imaging Performance Framework http://www.digitizationguidelines.gov/stillimages/documents/imaging.html
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Measurement Requirements
Measurements usually require some level of 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
X
X
X X
X X X X
X X
Aim – The point or set of points intended to be hit
Performance is more about consistency ( precision) than accuracy. In imaging, accuracy is often not absolute but rather a preference.
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Spatial Resolution Spatial Frequency Response (SFR) ISO 16067-1, 16067-2, 12233, 15529
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SIGNAL Any response that provides valued information
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What is Spatial Resolution ….and what are all those related terms ?
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• 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
• Number of pixels • Sampling frequency - dpi, ppi • *Limiting Resolution, Resolving Power • *Spatial Frequency Response - SFR • *Modulation Transfer Function - MTF
Related Metrics
*Related Standards – ISO 16067-1, 16067-2, 12233, & 15529 all employ these metrics
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Space and Frequency
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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 use space vs. frequency descriptions?
Compatible with engineering descriptions of information, bandwidth
Simplifies some forms of system analysis Compatible with several visual image quality descriptions
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Spatial Resolution
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• 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.
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- Limiting Resolution - ….so how does one measure it ?
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PROS * Simple to measure * Easily understood * Suitable for binary scanners
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
Increasing spatial frequency ( lp/mm or cycles/mm)
Yes, the features are readable
No, the features are illegible
Limiting Resolution= That spatial frequency at which finely spaced features are no longer visually detectable
Imaged target
Traditional method using bar targets and visual evaluation
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Sampling Frequency is not Resolution
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Increasing # lines/inch (line width = 1/200 “)
(line width = 1/300 “)
(line width = 1/400 “)
DPI (PPI) -> Quantity of data Resolution -> Quality of data (information)
Resolution Wedge Test Feature Example
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Sampling rate (DPI) is not Resolution real data example : results are not simulated
Resolution limit = whenever all five lines are undetectable
Though the sampling rate is increased to 600 dpi the true resolution for this scanner is only 300 dpi.
Resolution
Sampling Rate
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Spatial Frequency Response* (SFR) the better alternative
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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.
Limiting resolution will generally correspond to the spatial frequency having a 0.10 modulation transfer value
Increasing spatial frequency ( lp/mm or cycles/mm)
0.0
0.5
1.0
ModulationTransfer
Modulation Transfer = Target feature modulation after being imaged
Target feature modulation before being imaged
* Also referred to as the Modulation Transfer Function (MTF)
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Limiting resolution fails to predict quality&
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SFRs for RIT Targets
0
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0 2 4 6 8 10 12
Spatial Frequency ( cycles/mm)
Mod
ulat
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Tran
sfer
SFR for right targets
SFR for left targets
SFR for middle targets
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Anatomy of the SFR - regions of behavior -
Increasing spatial frequency (cycles/ ?? )Half-sampling frequency
Rel
ativ
e D
etai
l Con
trast
1.0
0.10 Rayleigh Resolution Criterion
Alia
sing
art
ifact
s ( j
aggi
es, m
oire
)
Lim
iting
re
solu
tion
Acu
tanc
e an
d Sh
arpn
ess
Sharpening operators (over-sharpening, halo-ing, scream)
Flar
e (h
aze)
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0.5
1.0
0.1
0.25 0.5Frequency, cy/pixel
SFR
high sharpness, low resolution
0
0.5
1.0
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0.25 0.5
low sharpness, high resolution
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0.25 0.5Frequency, cy/pixel
SFR
high sharpness, low resolution
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low sharpness, high resolution
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SFR
high sharpness, low resolution
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low sharpness, high resolution
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SFR
high sharpness, low resolution
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1.0
0.1
0.25 0.5
low sharpness, high resolution
Sharpness vs. Resolution
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How is the SFR Measured ?
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...all you need is an image of a GOOD slanted edge.
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Flatbed scanner using a linear array Horizontal (fast) and Vertical (slow) scan directions
fast
slow
Max. measurable frequency at 600 dpi sampling
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- SFR in Review -
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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
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Opto Electronic Conversion Function (OECF) ISO 14545 aka Tone Transfer Curve or Tone Reproduction Curve (TRC)
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SIGNAL Any response that provides valued information
ISO 22028-1
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Optical properties of objects
Density= - log10 (reflectance) * In practice, it is often more convenient to specify in terms of density, especially for densities greater than 1.0
Incident Illumination - 1000 units
1000 units reflected 1000 units incident = 1.000 reflectance
10 units reflected 1000 units incident
1 unit reflected 1000 units incident
100 units reflected 1000 units incident = 0.1000 reflectance
= 0.0100 reflectance
= 0.0010 reflectance
= 0.0 density
= 1.0 density
= 2.0 density
= 3.0 density
Reflective or transmissive hardcopy
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How is the OECF created ?
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(1) Scan grayscale step target with known optical reflectances*
neutral optical
reflectance
neutral optical density
0.010 20.013 1.90.016 1.80.020 1.70.025 1.60.033 1.480.042 1.380.051 1.290.065 1.190.083 1.080.107 0.970.132 0.880.166 0.780.209 0.680.263 0.580.339 0.470.437 0.360.550 0.260.708 0.150.912 0.04
(2) Compute average count value from the digital image file of scanned grayscale target
red green blue 8 8 7 13 13 14 18 17 18 24 25 25 31 32 30 38 40 39 45 46 45 52 51 51 60 62 61 69 70 69 81 82 81 92 93 92
105 105 104 119 120 120 135 132 132 153 155 154 172 173 172 194 193 193 219 220 220 247 250 249
average count value
(3) Table of reflectance – average count value data pairs for each color channel
neutral optical
reflectance red green blue
0.010 8 8 7 0.013 13 13 14 0.016 18 17 18 0.020 24 25 25 0.025 31 32 30 0.033 38 40 39 0.042 45 46 45 0.051 52 51 51 0.065 60 62 61 0.083 69 70 69 0.107 81 82 81 0.132 92 93 92 0.166 105 105 104 0.209 119 120 120 0.263 135 132 132 0.339 153 155 154 0.437 172 173 172 0.550 194 193 193 0.708 219 220 220 0.912 247 250 249
average count value
* 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.
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Graphical Representation of OECF
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OECF example
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0.000 0.200 0.400 0.600 0.800 1.000
neutral ( gray) reflectance
aver
age
gree
n co
unt v
alue
( 8
bit)
OECF example
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neutral (gray) densityav
erag
e gr
een
coun
t val
ue (
8 bi
t)
Reflectance Density Using Density is more revealing since the data values tend to be more evenly distributed
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Standards, Guidelines, Exposure - efficiently assigning density to count values -
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Typical Aim OECF Curves
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density
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ECI v2 gamma 2.2 gamma 1.8
Collection Specific OECF Aims‐ NARA Guidelines ‐
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NARA 2.0 max NARA 2.4 max
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
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White Balance / Neutrality -Keep all of the Neutrals neutral -
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• 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
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White Balance / Neutrality -Keep all of the Neutrals neutral -
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This is an example where the white balance performance is poor. Note how the blue channel OECF departs from the red and green OECF
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Question of Balance: Keep the Neutrals Neutral
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Question of Balance: Keep ^ the Neutrals Neutral
Neutral Tone Transfer - OECF
Note departure from neutrality in highlights
all of
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A few words on
Color Encoding Error ( ∆E)
- erroneously called color accuracy -
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• 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
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Example ∆E Analysis for Adobe RGB encoding
Summary measures for color encoding error
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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.
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Noise Random and otherwise
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NOISE Any response that detracts from a desired signal
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Scanner Noise Characterization
red green blue8 8 7
13 13 1418 17 1824 25 2531 32 3038 40 3945 46 4552 51 5160 62 6169 70 6981 82 8192 93 92105 105 104119 120 120135 132 132153 155 154172 173 172194 193 193219 220 220247 250 249
average count valuered green blue2.00 1.95 2.12.11 1.88 2.221.95 1.57 2.41.8 1.65 1.99
1.79 1.45 1.91.82 1.4 1.881.45 1.37 1.561.5 1.15 1.77
1.49 1.04 1.461.34 1.23 1.31.23 1.12 1.511.39 1 1.21.1 0.99 1.251.2 1.04 1.161 1.15 1.1
0.88 0.94 1.290.95 0.93 1.040.92 0.77 0.90.87 0.76 1.090.9 0.65 0.97
count value variability- noise
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OECF data RMS Noise data
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Scanner Noise Characterization
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RMS noise (CV)behavior for scanner
0.00
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Average Density
Cou
nt v
alue
dev
iatio
n
redgreenblue
density red green blue2.00 2.00 1.95 2.11.89 2.11 1.88 2.221.80 1.95 1.57 2.41.70 1.8 1.65 1.991.60 1.79 1.45 1.91.48 1.82 1.4 1.881.38 1.45 1.37 1.561.29 1.5 1.15 1.771.19 1.49 1.04 1.461.08 1.34 1.23 1.30.97 1.23 1.12 1.510.88 1.39 1 1.20.78 1.1 0.99 1.250.68 1.2 1.04 1.160.58 1 1.15 1.10.47 0.88 0.94 1.290.36 0.95 0.93 1.040.26 0.92 0.77 0.90.15 0.87 0.76 1.090.04 0.9 0.65 0.97
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Two Forms of Random Noise
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Fine-grain noise -> high frequency fluctuations
Course-grain noise -> low frequency fluctuations
Both of these images have the same amount of total noise
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One-dimensional random noise - banding and streaking -
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Following contrast enhancement
• Often found on devices using linear arrays • Contrast and equalize enhancements are useful in detecting such noise behaviors
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Artifacts
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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
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NOISE Any response that detracts from a desired signal
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Visual Inspection for Artifacts
Color Misregistration
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Visual assessments of spatial artifacts is quite reliable
Example: Visual inspection needed for adaptive processing artifacts
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Visual assessments of spatial artifacts is quite reliable Visual Inspection for Artifacts
optical resolution
limit
resolution limit due to CFA de-mosaicing
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Visual assessments of spatial artifacts is quite reliable
Visual Inspection for Artifacts
Bad Cam in a Linear array scanner
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Consider Intra-image and Inter-image Variation
Inter: image-to-image Intra: across image field
W.D.Wright, Photometry and the Eye, Hatton Press, 1949
OECF Color balance
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Intra-image: Across field
Device-level target
OECF Spatial frequency response
Variation in • OECF optical falloff • Effective resolution • Color misregistration
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References Frey and Reilly. [1999] Digital Imaging for Photographic Collections: Foundations
for Technical Standards. RIT, Image Permanence Institute. Frey/Chapman. [2000] “Developing Specifications for Archival Digital
Images.” IS&T 2001 PICS Conference Proceedings. Puglia/Rhodes. [2007] “Digitization for Preservation Reformatting of
Photographs (DRAFT).” Süsstrunk. [2002] “Color Encodings for Image Databases,” Proc. IS&T/SPIE's
Electronic Imaging: Internet Imaging, Vol. 4672, pp. 179-185. Süsstrunk. [2007] “Image Formation, Colorimetry, Color Spaces, Color
Encodings and Color Image Encodings, Color Management,” in Focal Encyclopedia of Photography: 4th Edition, Elsevier (Focal Press).
Süsstrunk. [2002] “Managing Colour in Digital Libraries,” in Colour Engineering: Achieving Device Independent Colour, John Wiley & Sons, Ltd.
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Thank you!
Paul Conway Associate Professor School of Information University of Michigan www.si.umich.edu
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