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

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Presentation on theme: "Comparing Cameras Using EMVA 1288 Dr. Friedrich Dierks Head of Software Development Components © Basler AG, 2006, Version 1.2."— Presentation transcript:

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

2 2 © Basler AG, 2006Dierks: EMVA 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.

3 3 © Basler AG, 2006Dierks: EMVA Outline  Some Basics  Temporal Noise  Spatial Noise

4 4 © Basler AG, 2006Dierks: EMVA 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

5 5 © Basler AG, 2006Dierks: EMVA 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

6 6 © Basler AG, 2006Dierks: EMVA 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 =

7 7 © Basler AG, 2006Dierks: EMVA 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

8 8 © Basler AG, 2006Dierks: EMVA Outline  Some Basics  Temporal Noise  Spatial Noise

9 9 © Basler AG, 2006Dierks: EMVA Light is Noisy N p = Number of photons collected in a single pixel during exposure time N p 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 N p = 6 photons

10 10 © Basler AG, 2006Dierks: EMVA SNR Diagram  Draw the SNR in a double-logarithmic diagram.  Take the logarithm to a base of 2.  SNR p yields a straight line with slope = ½.  Real cameras live right below the light’s SNR curve. No camera can yield a higher SNR than the light itself.

11 11 © Basler AG, 2006Dierks: EMVA Axes of the SNR Diagram Common units for SNR  SNR = x : 1  SNR bit = log 2 SNR = ln SNR / ln 2  SNR dB = 20 log 10 SNR = 6 SNR bit Special SNR values  Excellent *) SNR = 40:1 = 5…6 bit  Acceptable *) SNR = 10:1 = 3…4 bit  Threshold SNR = 1:1 = 0 bit Number of photons collected in one pixel during exposure time  Given as logarithm to the base of 2  Example µ p = 1000 ~ 1024 = 2 10  10 on the scale  +1  double exposure; -1  half exposure *) The definitions of “excellent” and “acceptable” SNR origin from ISO 12232

12 12 © Basler AG, 2006Dierks: EMVA 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) = QE [%] lambda [nm] blue  green  red  100%

13 13 © Basler AG, 2006Dierks: EMVA Quantum Efficiency in the SNR Diagram SNR e of the electrons SNR e is the SNR p curve is shifted to the right by |log 2 QE|. Examples: QE=50% = 1/2  shift by 1 QE=25% = 1/4  shift by 2 A high quantum efficiency yields a sensitive camera.

14 14 © Basler AG, 2006Dierks: EMVA 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). All scales are log 2 pixel saturates noise floor 11 1 analog signal bit 8 1 8bit subset min Gain Gain useful signal range 8 1 max Gain The saturation capacity depends on the Gain. no Gain *) Otherwise you get high fixed pattern noise at saturation.

15 15 © Basler AG, 2006Dierks: EMVA 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! Have at least ±1.5 DN noise. *) You can if you use loss-less compression

16 16 © Basler AG, 2006Dierks: EMVA Saturation in the SNR Diagram At saturation capacity SNR e becomes maximum. The corresponding number of photons saturating the camera is: Typical saturation capacity values are 30…100 ke - (“kilo electrons”). A high saturation capacity yields a good maximum image quality.

17 17 © Basler AG, 2006Dierks: EMVA 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 - *) Dark Noise is not to be confused with Dark Current Noise which is only a fraction of dark noise.

18 18 © Basler AG, 2006Dierks: EMVA Dark Noise in the SNR Diagram  SNR without photon noise:  SNR d yields a straight line with slope = 1.  The minimum detectable signal is found by convention at SNR d =1 *) were signal=noise. A low dark noise yields a sensitive camera. *) In the double-logarithmic diagram SNR=1 equals log(SNR) = 0

19 19 © Basler AG, 2006Dierks: EMVA The Complete SNR Diagram Overlaying photon noise and dark noise yields: with The curve starts at and ends at 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 µ e.sat [e - ]

20 20 © Basler AG, 2006Dierks: EMVA Dynamic Range Limits within one image  The brightest spot in the image is limited by µ p.sat  The darkest spot in the image is limited by µ p.min Dynamic Range = brightest / darkest spot *) This equation holds true only for sensors with a linear response. A high dynamic range is especially important for natural scenes. *)

21 21 © Basler AG, 2006Dierks: EMVA A Typical EMVA1288 Data Sheet Lots of Graphics

22 22 © Basler AG, 2006Dierks: EMVA 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

23 23 © Basler AG, 2006Dierks: EMVA 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

24 24 © Basler AG, 2006Dierks: EMVA How many Photons do I Have? The hard way to get #photons  Measure the radiance R  Compute µ p 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 µ p is proportional to T exp Typical values are 30fps) 30µs … 33ms  1:1000  10 bit  Lens aperture µ p 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 µ p is proportional to 1 / number of pixels 2MPixel : VGA  1 : 7  3 bit  Distance to Scene µ p is proportional to 1 / (distance to scene)^2

25 25 © Basler AG, 2006Dierks: EMVA 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.

26 26 © Basler AG, 2006Dierks: EMVA f 2d Example d d d f f 2f a 2a Start  pixel pitch a  focal length f  aperture diameter d  relative aperture f # = f / d  distance to object a o = 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#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.) aoao

27 27 © Basler AG, 2006Dierks: EMVA 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 More… *) Although not with respect to sensitivity

28 28 © Basler AG, 2006Dierks: EMVA Comparing Sensitivity without Graphics Rules of Thumb  For low light (SNR  1) compare µ p.min =  d / QE  For bright light (SNR>>1) compare QE Example  A102f (CCD) : QE = 56%,  d = 9 e -  µ p.min = 16 p ~  A600f (CMOS): QE = 32%,  d = 113 e -  µ p.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

29 29 © Basler AG, 2006Dierks: EMVA Outline  Some Basics  Temporal Noise  Spatial Noise

30 30 © Basler AG, 2006Dierks: EMVA 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 to the signal itself. offset gain + + grey valuelight Principal model of a single pixel

31 31 © Basler AG, 2006Dierks: EMVA Spatial Noise in the SNR Diagram Offset Noise  Adds to dark noise Gain Noise  New kind of behavior  Flat line in SNR diagram Resulting SNR formula

32 32 © Basler AG, 2006Dierks: EMVA Spatial Noise Effects Spatial Noise is relevant esp. for CMOS cameras. CMOS CCD

33 33 © Basler AG, 2006Dierks: EMVA 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

34 34 © Basler AG, 2006Dierks: EMVA 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

35 35 © Basler AG, 2006Dierks: EMVA 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 3 different cameras

36 36 © Basler AG, 2006Dierks: EMVA 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.

37 37 © Basler AG, 2006Dierks: EMVA Thank you for your attention! More info : > Technologies > EMVA 1288www.basler-vc.com Contact me :


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