I would like to know how the MSU - Video Quality measurement tool:


calculates the blur metric.

What could be the mathematical computation to calculate blur?

I want to do something similar in my Image pipeline.

EDIT: If I have a blurry image, I upsize it / downsize it, and then compute the blur again for this resampled image, would it be same/more/less?

  • $\begingroup$ This is related to photo.stackexchange.com/questions/9388/blur-detection $\endgroup$
    – chills42
    Mar 4, 2011 at 15:19
  • $\begingroup$ To the second part: it really depends on your resampling algorithm. $\endgroup$
    – mattdm
    Mar 4, 2011 at 16:25
  • $\begingroup$ I think this really belongs on the math.stackexchange.com or stackoverflow.com. While it may be related to another question here, that question in its own right is fairly off-topic. You are asking technical and mathematical questions that are far more likely to get a useful, viable answer at either of the above mentioned sites. I'm going to close this, and if you wish, I can migrate it to one of those sites for you. I would recommend the Math-SE site myself. $\endgroup$
    – jrista
    Mar 4, 2011 at 22:20

1 Answer 1


There are many possible blur metrics. For a list of some, see this student paper (from Stanford U). For many more, Google "blurring metric image". Most appear to measure some form of local contrast. (This suggests downsized images would be measured as less blurry than the originals and upsized as more blurry, but as @mattdm comments, the blurriness of the upsized image depends on the resampling algorithm.) Do not expect any simple mathematical formula. For example, one method first runs an edge detector over the image and then estimates the edge widths (wider = blurrier of course). For the details, and to answer the edited question, you would need to consult the MSU people, because they do not appear to have publicly documented their metric.


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