Comparing Cameras Using EMVA 1288
Dr. Friedrich Dierks
Head of Software Development Components
© Basler AG, 2006, Version 1.2
www.standard1288.org
Why Attend this Presentation?
After attending this presentation you can…
compare the sensitivity of cameras
with respect to temporal and spatial noise
using EMVA 1288 data sheets.
You understand the role of
Gain (doesn’t matter)
Pixel size (doesn’t matter)
Bright light (the key)
Beware : All formulas in this presentation will drop out of the sky 
For details see the standard and the white papers.
© Basler AG, 2006
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Outline
Some Basics
Temporal Noise
Spatial Noise
© Basler AG, 2006
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Gain is not Sensitivity
Example:
Camera A
Camera B
Camera A yields an image twice as bright as camera B
 Does that mean that camera A is twice as sensitive as camera B? No!
Increase the Gain of camera B until the images have equal brightness (Gain=2)
 Does that mean camera B is now as sensitive as camera A ?
 No! Multiplying each pixel x2 in software has the same effect…
The Gain has no effect on the sensitivity of a camera*).
© Basler AG, 2006
*)
At least with today’s digital cameras 4
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What is Sensitivity?
Example:
A : 10 ms exposure
B : 20 ms exposure
Camera A yields the same image quality as camera B.
Camera A needs half the amount of light as camera B in order to achieve that.
Camera A is twice as sensitive as camera B !
Sensitivity is the ability to deliver high image quality on low light.
© Basler AG, 2006
5
Dierks: EMVA 1288
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Defining Image Quality
Image Quality = Signal-to-Noise Ratio (SNR)
=
bright signal – dark signal
noise
SNR does not depend on Gain.
Gain increases signal as well as noise.
SNR does not depend on Offset.
Offset shifts dark signal as well as bright signal.
There are different kinds of noise:
total noise = temporal noise + spatial noise
© Basler AG, 2006
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Different Kinds of Noise
Total Noise
Variation (= non-uniformity)
between the grey values of
pixels in a single frame.
x, y
Spatial Noise
Variation between the grey
values of pixels if the temporal
noise is averaged out.
x, y
Temporal Noise
Variation (=flicker) in the grey
value of the pixels from frame
to frame.
© Basler AG, 2006
x, y
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Outline
Some Basics
Temporal Noise
Spatial Noise
© Basler AG, 2006
8
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Light is Noisy
Np = 6 photons
light
source
exposure time
Np = Number of photons
collected in a single pixel
during exposure time
Np varies from measurement to
measurement.
Light itself is noisy.
© Basler AG, 2006
Physics of light yields:
SNR p   p
with mean number of photons
Image quality ~
p .
amount of light
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SNR Diagram
log2 SNR [bit]
Draw the SNR in a
double-logarithmic diagram.
SNRp   p
5
Rp
SN
Take the logarithm to a base of 2.
SNRp yields a straight line
with slope = ½.
e
: th
elf
s
t
i
t
ligh
slope = 1/2
real cameras
1
1
5
10 log p [bit]
2
Real cameras live right below the light’s SNR curve.
No camera can yield a higher SNR than the light itself.
© Basler AG, 2006
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Axes of the SNR Diagram
Common units for SNR
SNR
log2 SNR [bit]
=x:1
SNRbit = log2 SNR = ln SNR / ln 2
SNRdB = 20 log10 SNR = 6 SNRbit
Special SNR values
Excellent*) SNR = 40:1 = 5…6 bit
Acceptable*) SNR = 10:1 = 3…4 bit
Threshold SNR = 1:1 = 0 bit
Rp
SN
excellent
5
acceptable
1
1
5
10 log2 p [bit]
Number of photons collected in one pixel during exposure time
 Given as logarithm to the base of 2
 Example µp = 1000 ~ 1024 = 210  10 on the scale
 +1  double exposure; -1  half exposure
© Basler AG, 2006
*)
The definitions of “excellent” and “acceptable” SNR origin from ISO 12232
11
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Quantum Efficiency
Not every photon hitting a pixel creates a free electron.
Quantum Efficiency (QE) =
number of electrons collected
number of photons hitting the pixel
QE heavily depends on the wavelength.
EMVA 1288 gives QE as table or diagram.
 100%
QE [%]
QE < 100% degrades the SNR of a camera
SNRe  QE SNRp
blue  green  red
lambda [nm]
Typical max QE values : 25% (CMOS) … 60% (CCD)
© Basler AG, 2006
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Dierks: EMVA 1288
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Quantum Efficiency in the SNR Diagram
log2 SNR [bit]
SNRe of the electrons
SNRe  QE SNRp
SNRe is the SNRp curve is
shifted to the right by |log2 QE|.
elf
s
t
i
ns
ght
i
o
l
r
t
lec
the
:
e
d
Rp
cte
SN
e
l
l
co
:
Re
SN
5
1
shift by log2 QE
1
Examples:
QE=50% = 1/2  shift by 1
QE=25% = 1/4  shift by 2
5
10 log p [bit]
2
A high quantum efficiency yields a sensitive camera.
© Basler AG, 2006
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Saturation
analog
signal
 A camera saturates…
 if the pixel saturates
 if the analog-to-digital converter saturates
 The useful signal range lies between
saturation and the noise floor
 At minimum Gain the ADC saturates
shortly before the pixel*)
11
*)
no
Gain
min
Gain
12
8
max
Gain
Gain
useful
signal
range
8
1
1
1
1
noise floor
The saturation capacity depends on the Gain.
© Basler AG, 2006
8bit subset
pixel
saturates
 The number of electrons at saturation
is the Saturation Capacity  e. sat
 Do not confuse saturation capacity
with full well capacity (pixel only).
12 bit
Otherwise you get high fixed pattern noise at saturation.
All scales are log2
14
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Quantization Noise
2 y
q
Rule of thumb: the dark noise
must be larger than 0.5
 y  0.5
1
 
-0.5
y
0 
0.5
y
-1
Corollary: With a N bit digital
signal you can deliver no more*)
than N+1 bit dynamic range.
-2
2 y
Example : A102f camera with
11 bit dynamic range will deliver
only 9 bit in Mono8 mode. Use
Mono16!
q
yq = 0 = const
1
yq = 01 = toggeling
mean=0
mean=0.5
-0.5
0.5
0
y
-1
Have at least ±1.5 DN noise.
© Basler AG, 2006
*)
You can if you use loss-less compression
-2
15
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Saturation in the SNR Diagram
At saturation capacity
SNRe becomes maximum.
log2 SNR [bit]
1/2 log2 µe.sat
SNRe. max  e. sat
5
The corresponding number of
photons saturating the camera is:
 p. sat 
e. sat
QE
Rp
SN
1
1
log2 µp
[bit]
Re
SN
5
log2 µp.sat
10
Typical saturation capacity values are 30…100 ke- (“kilo electrons”).
A high saturation capacity yields a good maximum image quality.
© Basler AG, 2006
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Dierks: EMVA 1288
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Dark Noise
EMVA 1288 model assumption:
Camera noise = photon noise + dark noise*)
Dark noise = constant
Dark noise is measured by the standard deviation of the
dark signal in electrons [e-]
 d  const
The model approximates real world cameras pretty good for
reasonable exposure times and reasonable sensor temperature.
Typical dark noise values are 7…110 e*)
© Basler AG, 2006
Dark Noise is not to be confused with Dark Current Noise which is only a fraction of dark noise.
17
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Dark Noise in the SNR Diagram
SNR without photon noise:
QE
d
p
slope=1
se
SNRd 
log2 SNR [bit]
The minimum detectable
signal

 p. min  d
1
1
d
Rp
SN
SN
R
SNRd yields a straight line with
slope = 1.
:d
ar
k
no
i
5
log2 µp.min
10
log2 µp [bit]
QE
is found by convention at
SNRd=1*) were signal=noise.
*)
© Basler AG, 2006
A low dark noise yields
a sensitive camera.
In the double-logarithmic diagram SNR=1 equals log(SNR) = 0
18
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The Complete SNR Diagram
SNRe 
with
QE  p
5
QE  p   d2
the
1
 p. min   p   p. sat
The curve starts at

 p. min  d
QE
and ends at
 p. sat 
© Basler AG, 2006
log2 SNRmax
e. sat
QE
1
ti
ligh
tse
lf
u
tim
(op
5
m)
s
da l op
do rk e =
m no 1
in is
at e
ed
Overlaying photon noise
and dark noise yields:
log2 SNR [bit]
10
1/2
=
ise
pe
slo ton no d
te
pho mina
do
15
log2 µp
[bit]
log2 µp.sat
log2 µp.min
An EMVA 1288 data sheet provides all parameters to
draw the curve, e.g. in Excel:
 Quantum efficiency QE [%] as a function of wavelength
 Dark noise d [e-]
 Saturation capacity µe.sat [e-]
19
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Dynamic Range
Limits within one image
 The brightest spot in the
image is limited by µp.sat
 The darkest spot in the
image is limited by µp.min
Dynamic Range
= brightest / darkest spot
 p. sat e. sat
DYN 

 p. min
d
log2 SNR [bit]
log2 DYN
5
the
1
1
ti
ligh
tse
lf
u
tim
(op
5
m)
10
15
log2 µp
[bit]
log2 µp.sat
log2 µp.min
*)
A high dynamic range is especially important for natural scenes.
*)
© Basler AG, 2006
This equation holds true only for sensors with a linear response.
20
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A Typical EMVA1288 Data Sheet
Lots of Graphics
© Basler AG, 2006
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Were Does the Data Come From?
Example : At Basler a fully automated
camera test tool ensures quality in production
Every camera produced will be EMVA 1288
characterized (done for 1394 and GigE already)
Customer benefits
 Guaranteed quality
 Full process control
 Parameters can be given typical + range range
Other manufacturers have similar measuring
devices in production
© Basler AG, 2006
22
Dierks: EMVA 1288
18
50
1,6
45
0,6
1,1
4,7
57
16
353
11,0
8,8
7,1
7,8
6,7
6,4
192
0
VideoFormat
15,0
17,3
Offset [raw]
SNR.total max [bit]
SNR.temp max [bit]
Abs. Sensitivity [p~]
1 / Conversin Gain [e+/DN]
PRNU.1288 [%]
DSNU.1288 [e-]
9
113
Sat. capacity [ke-]
56%
32%
Gain [raw]
11,0
5,0
2,5
5,8
6,45
9,9
# photons sat [bit]
# photons [bit]
A : SNR A102f temporal [bit]
B : SNR A60xf temporal [bit]
SNR CamA / CamB
15
100
Dark noise [e-]
Wavelength [nm]
temporal CCD 1.45 M
temporal CMOS VGA
temporal
545 spatial
QE [%] @ 545 nm
Frame Rate [fps]
Resolution
Sensor Type
A102f
A60xf
Pixel size [µm]
Camera A
Camera B
Noise Type
Camera Type
The Camera Comparer
Dynamic range [bit]
www.standard1288.org
11 Mono16
768 Mono16
SNR Diagram
A102f : Y [DN]
A102f : Y.dark [DN]
A102f : µ.p [bit]
160
10
5,8 bit
 Select cameras A and B
 Select wavelength
(white  545 nm = green)
 Select SNR want
 read #photon ratio
 Select #photons have
 read SNR ratio
© Basler AG, 2006
SNR Light
SNR A102f temporal
SNR A60xf temporal
Photon Cursor
SNR Cursor
9
8
7
SNR [bit]
SNR want [bit]
5,0
A : #photon A102f temporal [bit]10,9
B : #photon A60xf temporal [bit]13,7
#photons CamB / CamA
6,6
10
6
5
4
3
2
1
0
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19
# photons collected [bit]
23
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How many Photons do I Have?
The hard way to get #photons
 Measure the radiance R
 Compute µp
The easy way to get #photons
 Use EMVA1288 characterized camera
to measure #photons
y  QE  K  µ p
 y : grey value in digital numbers [DN]
 read from viewer
 QE : quantum efficiency for given
wavelength (white light is tricky…)
 get from data sheet
 K : conversion gain for operating point
used for characterization (esp. Gain)
 get from data sheet
© Basler AG, 2006
Some ways to influence #photons
 Exposure time
µp is proportional to Texp
Typical values are (@ 30fps)
30µs … 33ms  1:1000  10 bit
 Lens aperture
µp is proportional to (1/f#)^2
Typical f-stops are
16, 11, 8, 5.6, 4, 2.8, 2, 1.4  1 : 128  7 bit
 Resolution
µp is proportional to 1 / number of pixels
2MPixel : VGA  1 : 7  3 bit
 Distance to Scene
µp is proportional to 1 / (distance to scene)^2
24
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The Pixel Size Myth…
object
lens
lens

sensor
P
P
d
d
ao
f
 A patch on the object’s surface
radiates light
 The lens focuses the light to the corresponding
pixel no matter how large the pixel is
 The lens catches a certain amount of
light depending on the solid angle
 For a fair comparison of cameras…
 d 2 
2

ao2
  f 
 2  
4 ao  f # 
2


keep the resolution constant
 larger pixels require larger focal length
keep the aperture diameter d = f / f# constant
 larger pixels have larger relative aperture
Larger Pixels DO NOT result in a more sensitive camera.
© Basler AG, 2006
25
Dierks: EMVA 1288
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Example
Start
 pixel pitch a
 focal length f
 aperture diameter d
 relative aperture f# = f / d
 distance to object ao = const
ao
d
f#
a
f
d
f#
Step 1 : double pixel pitch a  2a
 yields four times the amount of light
 because of quarter number of pixels
2a
f
2d
2a
f#
2f
d
2f#
© Basler AG, 2006
2a
2f
Step 2 : double focal length f  2f
while relative aperture f# = const
 back to original number of pixels
 yields four times the amount of light
 because of twice the aperture diameter
Step 3 : double relative aperture f#  2f#
 yields same amount of light
 because of original number of pixels
 because of original aperture diameter d
 although the pixel pitch is doubled
(q.e.d.)
26
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EMVA 1288
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Don’t Get Confused - Pixel Size Matters a Lot*)
For example smaller pixels…
yield less aberrations because of near-axis optics
yield smaller and cheaper optics
allow larger number of pixels
have less problems with micro lenses
For example larger pixels…
yield sharper images because less resolving power of the lens is
required
keep you out of the refraction limit of the lens
have a better geometrical fill factor (area scan)
have a larger full well capacity
*)
© Basler AG, 2006
Although not with respect to sensitivity 
27
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Comparing Sensitivity without Graphics
log2 SNR
Rules of Thumb
For low light (SNR1)
compare µp.min = d / QE
5
For bright light (SNR>>1)
compare QE
1
A
1
 log2 QE
Example
A102f (CCD)
A600f (CMOS)
5
log2 p
B
10
15
 log2 µp.min
: QE = 56%, d = 9 e µp.min= 16 p~
: QE = 32%, d = 113 e-  µp.min= 353 p~
For low light the A102f is 22 (=353/16) times more sensitive than the A600f
For bright light the A102f is 1.8 (=56/32) times more sensitive than the A600f
© Basler AG, 2006
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Outline
Some Basics
Temporal Noise
Spatial Noise
© Basler AG, 2006
29
Dierks: EMVA 1288
www.standard1288.org
Spatial Noise
Principal model of a single pixel
light
+
gain
grey value
+
offset
The offset differs from pixel to pixel  add offset noise
The gain differs from pixel to pixel  add gain noise
DSNU
PRNU e
y
Gain noise is proportional to
the signal itself.
grey value noise
gain
noise
constant
light level
© Basler AG, 2006
p
30
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Spatial Noise in the SNR Diagram
log2 SNR [bit]
Offset Noise
Adds to dark noise
slope = 0
gain noise
dominated
log2 SNRmax
 d2  DSNU 2
Gain Noise
New kind of behavior
SNRd 
µp
PRNU µ p

th e
1
 const
PRNU
Flat line in SNR diagram
log2 SNR [bit]
1
-log2 PRNU
tse
lf
5
)
10
log2 µp
[bit]
15
log2 µp.sat
Resulting SNR formula
QE   
p
log2 µp [bit]
um
log2 µp.min
SNRe 
slope = 0
© Basler AG, 2006
1
ti
ligh
tim
(op
s
da l op
do rk e =
m no 1
in is
at e
ed
5
/2
= 1 ise
e
p
slo ton no d
te
o
ph mina
o
d
QE  p
2
d
 DSNU 2   PRNU 2 QE 2  p2 
SNRe. max  e. sat 1  PRNU 2 e. sat 
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Dierks: EMVA 1288
10
SNR Light
SNR A102f temporal
SNR A102f spatial
Photon Cursor
SNR Cursor
9
www.standard1288.org
8
7
10
SNR Light
SNR A60xf temporal
SNR A60xf spatial
Photon Cursor
SNR Cursor
9
8
SNR [bit]
7
5
SNR Diagram
4
3
8
2
7
SNR Light
SNR A102f temporal
SNR A102f spatial
Photon Cursor
SNR Cursor
CCD
1
6
CMOS
10
9
0
6
0
1
2
3
4
5
5
6
7
8
9
SNR [bit]
SNR [bit]
6
Spatial Noise Effects
5
10 114 12 13 14 15 16 17 18
# photons collected 3[bit]
4
2
3
1
2
0
1
0
1
2
3
4
5
0
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19
# photons collected [bit]
Spatial Noise is relevant esp. for CMOS cameras.
© Basler AG, 2006
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Dierks: EMVA 1288
6
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Pixel Correction
Spatial nose can be
corrected inside a camera.
Each pixel get it’s own offset
to compensate for DSNU…
CCD without shading
..and it’s own gain to
compensate for PRNU
Most CMOS cameras have
a pixel correction
Depending on the sensor
even more correction types
are required
CMOS with shading
operating point were the
correction values have been taken
© Basler AG, 2006
33
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Stripes
image with
diagonal stripes
time
gap between
line readouts
EMI based stripes
 High frequency disturbing signal
is added to the video signal
 The maxima of the disturbing
signal are shifted between lines
 This results in diagonal stripes
which tend to move and pivot
with temperature
Structure based stripes
odd columns
ADC
 There are multiple signal paths
in the sensor/camera with
slightly different parameters
(gain, offset)
 This results in fixed horizontal or
vertical stripes
 Example: even-odd-mismatch
ADC
even columns
© Basler AG, 2006
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The Spectrogram
lines
1600
FFT
2
FFT
...
...
N
FFT
3 different cameras
1400
Mean
FFT Amplitude [#photons]
1
1200
1000
800
600
400
200
2,05
2,15
2,27
2,41
2,56
2,73
2,92
3,15
3,41
3,72
4,09
4,55
5,12
5,85
6,82
8,18
10,23
13,64
20,46
40,92
periode
infinite
0
Period Length [pixels]
X-Axis : horizontal distance between stripes in [pixel]
Y-Axis : amplitude at the corresponding frequency in #photons
The ideal camera has white noise only  flat spectrogram


Noise floor height indicates minimum detectable signal
Peaks indicate stripes in the image
© Basler AG, 2006
35
Dierks: EMVA 1288
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Conclusion
With EMVA 1288 data sheet you can…
 compare the sensitivity of cameras
 with respect to temporal and spatial noise
Remember:
 Gain doesn’t matter
 Pixel size doesn’t matter
 Nothing beats having enough light 
Get Started:
 Get the camera comparer and play around with the parameters.
 Get a camera with EMVA1288 data sheet and determine
the #photons in your application.
© Basler AG, 2006
36
Dierks: EMVA 1288
www.standard1288.org
Thank you for your attention!
More info : www.basler-vc.com > Technologies > EMVA 1288
Contact me : friedrich.dierks@baslerweb.com
© Basler AG, 2006
37
Dierks: EMVA 1288