Mir
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Mir includes a utility called mirvanity
that can use a high speed camera to measure the visual latency of your display (from client rendering to your eye).
It is called "mirvanity" because it works by rendering a pattern to the screen and then looking at the screen itself via the camera. In theory you could also use a laptop screen and webcam with a mirror, however laptop webcams tend to be too slow for this task. In order to use mirvanity
you need a high speed camera supported by Linux, such as a PlayStation Eye.
mirvanity
A common question is how accurate mirvanity's results are. An LCD monitor typically refreshes at 60Hz on an interval of about 16.6ms, and you have the added variability of the camera which even at high speed has a frame interval of about 5ms. So surely you have at least 22ms of variability?
Yes indeed instantaneous measurements will have wide variability that is the sum of the display and camera frame intervals. However both of these devices are very precise even though they're not in phase. So using many samples over a short period of time, mirvanity
calculates the expected variability and compares it to the measured variability. After the expectation starts to match the measurement you have a good estimate of the baseline latency and estimated error range. mirvanity
prints out this error and for a typical 60Hz monitor and common 187Hz PlayStation Eye camera, we observe pretty much the variability expected of around 22ms.
Knowing this variability is the sum of both waves, you can simply take the trough (or peak) as your measurement and thus eliminate the variability of the display and camera from the results. If you take the trough, you are excluding all display and camera latency. If you take the peak then you are including worst case display and camera latency. mirvanity
reports all of these numbers for completeness. This is based on the Superposition Principle and typically yields a stable measurement with approximately 3ms or less random variation.
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Generated on Mon Mar 27 12:06:55 UTC 2017