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Comparing Cameras Using EMVA 1288 Dr. Friedrich Dierks Head of Software Development Components © Basler AG, 2006, Version 1.2
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 © Basler AG, 2006Dierks: EMVA Outline Some Basics Temporal Noise Spatial Noise
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 © 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 © 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 © 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 © Basler AG, 2006Dierks: EMVA Outline Some Basics Temporal Noise Spatial Noise
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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © Basler AG, 2006Dierks: EMVA A Typical EMVA1288 Data Sheet Lots of Graphics
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 © 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 © 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 © 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 © 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 © 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 © 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 © Basler AG, 2006Dierks: EMVA Outline Some Basics Temporal Noise Spatial Noise
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 © 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 © Basler AG, 2006Dierks: EMVA Spatial Noise Effects Spatial Noise is relevant esp. for CMOS cameras. CMOS CCD
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 © 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 © 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 © 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 © Basler AG, 2006Dierks: EMVA Thank you for your attention! More info : > Technologies > EMVA 1288www.basler-vc.com Contact me :
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