Comparing Cameras Using EMVA 1288 Dr. Friedrich Dierks Head of Software Development Components © Basler AG, 2006, Version 1.2

Comparing Cameras Using EMVA 1288

Dr. Friedrich Dierks Head of Software Development Components

© Basler AG, 2006, Version 1.2

2© Basler AG, 2006 Dierks: EMVA 1288

www.standard1288.org

Why Attend this Presentation?

After attending this presentation you can…compare the sensitivity of cameraswith respect to temporal and spatial noiseusing 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.



Outline

Some BasicsTemporal Noise Spatial Noise



Gain is not Sensitivity

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…

Camera A

Camera B

Example:

The Gain has no effect on the sensitivity of a camera*).

*) At least with today’s digital cameras



What is Sensitivity?

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 !

Example:

Sensitivity is the ability to deliver high image quality on low light.

A : 10 ms exposure

B : 20 ms exposure



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

=



Different Kinds of Noise

Total Noise Variation (= non-uniformity)

between the grey values of pixels in a single frame.

Spatial Noise Variation between the grey

values of pixels if the temporal noise is averaged out.

Temporal Noise Variation (=flicker) in the grey

value of the pixels from frame to frame.

x, y

x, y

x, y



Outline




Light is Noisy

Np = Number of photons collected in a single pixel during exposure time

Np varies from measurement to measurement.

Light itself is noisy.

Physics of light yields:

with mean number of photons .

Image quality ~ amount of light

light source

exposure time

Np = 6 photons

ppSNR

p



SNR Diagram

Draw the SNR in a double-logarithmic diagram.

Take the logarithm to a base of 2.

SNRp yields a straight line with slope = ½.

Real cameras live right below the light’s SNR curve.

ppSNR

No camera can yield a higher SNR than the light itself.



Axes of the SNR Diagram

Common units for SNRSNR = x : 1

SNRbit = log2 SNR = ln SNR / ln 2

SNRdB = 20 log10 SNR = 6 SNRbit

Special SNR valuesExcellent*) SNR = 40:1 = 5…6 bitAcceptable*) SNR = 10:1 = 3…4 bitThreshold SNR = 1:1 = 0 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

*) The definitions of “excellent” and “acceptable” SNR origin from ISO 12232



Quantum Efficiency

Not every photon hitting a pixel creates a free electron.

number of electrons collected

number of photons hitting the pixel

QE heavily depends on the wavelength.EMVA 1288 gives QE as table or diagram.

QE < 100% degrades the SNR of a camera

Typical max QE values : 25% (CMOS) … 60% (CCD)

Quantum Efficiency (QE) =

pe SNRQESNR

QE [%]

lambda [nm]

blue green red

100%



Quantum Efficiency in the SNR Diagram

SNRe of the electrons

SNRe is the SNRp curve is shifted to the right by |log2 QE|.

Examples:

QE=50% = 1/2 shift by 1

QE=25% = 1/4 shift by 2

pe SNRQESNR

A high quantum efficiency yields a sensitive camera.



Saturation

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*)

The number of electrons at saturation is the Saturation Capacity

Do not confuse saturation capacity with full well capacity (pixel only).

sate.

All scales are log2

pixel saturates

noise floor

11

1

analogsignal

12

1

12 bit

8

1

8bit subset

minGain

Gain

usefulsignalrange

8

1

maxGain

The saturation capacity depends on the Gain.

noGain

*) Otherwise you get high fixed pattern noise at saturation.



Quantization Noise

Rule of thumb: the dark noise must be larger than 0.5

Corollary: With a N bit digital signal you can deliver no more*) than N+1 bit dynamic range.

Example : A102f camera with 11 bit dynamic range will deliver only 9 bit in Mono8 mode. Use Mono16!

y

y

yq

1

2

-1

-2

0 0.5-0.55.0y

Have at least ±1.5 DN noise.

*) You can if you use loss-less compression

y

yq

1

2

-1

-2

0 0.5-0.5

yq = 0 = const

mean=0yq = 01 = toggeling

mean=0.5



Saturation in the SNR Diagram

At saturation capacity SNRe becomes maximum.

The corresponding number of photons saturating the camera is:

Typical saturation capacity values are 30…100 ke- (“kilo electrons”).

sateeSNR .max.

A high saturation capacity yields a good maximum image quality.

QEsate

satp.

.



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-]

The model approximates real world cameras pretty good for reasonable exposure times and reasonable sensor temperature.

Typical dark noise values are 7…110 e-

constd

*) Dark Noise is not to be confused with Dark Current Noise which is only a fraction of dark noise.



Dark Noise in the SNR Diagram

SNR without photon noise:

SNRd yields a straight line with slope = 1.

The minimum detectable signal

is found by convention at SNRd=1*) were signal=noise.

pd

d

QESNR

QEd

p

min.

A low dark noise yields

a sensitive camera.

*) In the double-logarithmic diagram SNR=1 equals log(SNR) = 0



The Complete SNR DiagramOverlaying photon noise and dark noise yields:

with

The curve starts at

and ends at

2dp

pe

QE

QESNR

log2 SNR [bit]

slope = 1/2

photon noise

dominated

slope

= 1

dark

noi

se

dom

inat

ed

51 10 151

5

the light itself

(optimum)

log2 µp.min log2 µp.sat

log2 µp [bit]

log2 SNRmax

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-]

satppp .min.

QEd

p

min.

QEsate

satp.

.



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

*) This equation holds true only for sensors with a linear response.

log2 SNR [bit]

51 10 151

5

log2 DYN

the light itself

(optimum)


log2 µp [bit]

d

sate

p

satpDYN

.

min.

.

A high dynamic range is especially important for natural scenes.

*)



A Typical EMVA1288 Data Sheet

Lots of Graphics



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



Cam

era

Typ

e

No

ise

Typ

e

Sen

sor

Ty

pe

Res

olu

tio

n

Fra

me

Ra

te [

fps

]

Pix

el s

ize

[µm

]

QE

[%

] @

54

5 n

m

Dar

k n

ois

e [e

-]

Sat

. ca

pac

ity

[k

e-]

DS

NU

.128

8 [e

-]

PR

NU

.128

8 [%

]

1 / C

on

vers

in G

ain

[e

+/D

N]

Ab

s. S

en

sit

ivit

y [

p~

]

Dyn

am

ic r

ang

e [b

it]

SN

R.t

em

p m

ax [

bit

]

SN

R.t

ota

l m

ax

[bit

]

# p

ho

ton

s s

at

[bit

]

Gai

n [

raw

]

Off

set

[ra

w]

Vid

eo

Fo

rmat

Camera A A102f temporal CCD 1.45 M 15 6,45 56% 9 18 1,6 0,6 4,7 16 11,0 7,1 6,7 15,0 192 11 Mono16Camera B A60xf temporal CMOS VGA 100 9,9 32% 113 50 45 1,1 57 353 8,8 7,8 6,4 17,3 0 768 Mono16

temporalWavelength [nm] 545 spatial

# photons [bit] 11,0A : SNR A102f temporal [bit] 5,0B : SNR A60xf temporal [bit] 2,5SNR CamA / CamB 5,8

SNR want [bit] 5,0A : #photon A102f temporal [bit]10,9B : #photon A60xf temporal [bit]13,7#photons CamB / CamA 6,6

A102f : Y [DN] 160A102f : Y.dark [DN] 10A102f : µ.p [bit] 5,8 bit

SNR Diagram

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

# photons collected [bit]

SN

R [

bit]

SNR Light

SNR A102f temporal

SNR A60xf temporal

Photon Cursor

SNR Cursor

The Camera Comparer

Select cameras A and B Select wavelength

(white 545 nm = green) Select SNR want

read #photon ratio Select #photons have

read SNR ratio



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 : 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

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

pµKQEy



The Pixel Size Myth…

A patch on the object’s surface radiates light

The lens catches a certain amount of light depending on the solid angle

The lens focuses the light to the corresponding pixel no matter how large the pixel is

For a fair comparison of cameras… 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.

2

#22

2

4

2

f

f

aa

d

oo



2f

2d

Example

d

d

d

f

f

2f

a

2a

2a

2a

Start pixel pitch a focal length f aperture diameter d relative aperture f# = f / d

distance to object ao = const

Step 1 : double pixel pitch a 2a yields four times the amount of light because of quarter number of pixels

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

f#

f#

f#

2f#

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.)

ao



Don’t Get Confused - Pixel Size Matters a Lot*)

For example smaller pixels…yield less aberrations because of near-axis opticsyield smaller and cheaper opticsallow larger number of pixelshave less problems with micro lenses

For example larger pixels…yield sharper images because less resolving power of the lens is requiredkeep you out of the refraction limit of the lenshave a better geometrical fill factor (area scan)have a larger full well capacity

More…*) Although not with respect to sensitivity



Comparing Sensitivity without Graphics

Rules of Thumb

For low light (SNR1) compare µp.min = d / QE

For bright light (SNR>>1) compare QE

Example A102f (CCD) : QE = 56%, d = 9 e- µp.min= 16 p~

A600f (CMOS) : QE = 32%, d = 113 e- µp.min= 353 p~

For low light the A102f is 22 (=353/16) times more sensitive than the A600fFor bright light the A102f is 1.8 (=56/32) times more sensitive than the A600f



Outline




Spatial Noise

The offset differs from pixel to pixel add offset noise DSNU

The gain differs from pixel to pixel add gain noise

Gain noise is proportional tothe signal itself.

offset

gain++

grey valuelight

Principal model of a single pixel

p

y

gainnoise

constantlight level

grey value noise

ePRNU



Spatial Noise in the SNR DiagramOffset NoiseAdds to dark noise

Gain NoiseNew kind of behavior

Flat line in SNR diagram

log2 SNR [bit]

slope = 1/2

photon noise

dominated

slope

= 1

dark

noi

se

dom

inat

ed

51 10 151

5

the light itself

(optim

um)


log2 µp [bit]

log2 SNRmax

slope = 0gain noisedominated

22222pdp

pe

QEPRNUDSNUQE

QESNR

22 DSNUd

constPRNUµPRNU

µSNR

p

pd

1

Resulting SNR formula

satesatee PRNUSNR .2

.max. 1



Spatial Noise Effects

Spatial Noise is relevant esp. for CMOS cameras.

SNR Diagram

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

# photons collected [bit]

SN

R [

bit]

SNR Light

SNR A60xf temporal

SNR A60xf spatial

Photon Cursor

SNR Cursor0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

SNR Light

SNR A102f temporal

SNR A102f spatial

Photon Cursor

SNR Cursor

CMOS

CCD



Pixel Correction

Spatial nose can be corrected inside a camera.

Each pixel get it’s own offset to compensate for DSNU…

..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

CCD without shading

operating point were the correction values have been taken



Stripes

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 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

gap betweenline readouts

timeimage with

diagonal stripes

odd columns

even columns

ADC

ADC



The Spectrogram

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

0

200

400

600

800

1000

1200

1400

1600

infin

ite

40

,92

20

,46

13

,64

10

,23

8,1

8

6,8

2

5,8

5

5,1

2

4,5

5

4,0

9

3,7

2

3,4

1

3,1

5

2,9

2

2,7

3

2,5

6

2,4

1

2,2

7

2,1

5

2,0

5

Period Length [pixels]

FF

T A

mp

litu

de

[#

ph

oto

ns

]

3 different cameras



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.



Thank you for your attention!

More info : www.basler-vc.com > Technologies > EMVA 1288

Contact me : [email protected]

Documents

Comparing Cameras Using EMVA 1288 Dr. Friedrich Dierks Head of Software Development Components © Basler AG, 2006, Version 1.2