I wonder naively: if you can make rules, why train something to learn
them vs. just implementing them directly? I'm really curious if there's
an advantage in logistics or performance (can meaningful extrapolation
somehow occur?).
I think the answer for machine learning is not to make rules, but to
gather examples based on perceptual experiments, assuming what you are
after is noticeability. In that case, you will likely allow dropouts (I
assume black pixels) more when they are in dark areas, I imagine, which
may not be desireable, or might save the corporation a few pennies. :-)
Those perceptual experiments might be costly, would save the angst of
getting the rules right, and I wonder what sort of Quality Index you
might derive beyond pass/fail. The data might be leveraged for other
applications. Or maybe you have existing data that could be used to train?
I've only made general comparisons of images (using color histograms at
the moment for my interactive image associator), but have the QA
background to appreciate the motivation. I'd love to stay on top of it
if a fellow learner could be of use.
Regards,
Bill
On 4/3/17 11:04 AM, mat saunders wrote:
Hi,
I am using SSIM to compare 2 video streams\sets of images and I find
it to be almost too accurate. I would like some fudge factor like
other image comparison tools have. I used to do it in an automated
test suite but due to file sizes and amounts I turned to scikit.
I do quality assurance on a render engine and we just want to make
sure the images are meaningfully identical build to build. Currently
with SSIM I am seeing things as small as 4 pixels across a 1920x1080
image different. I personally would like to ignore those 4 pixels but
still catch meaningful items. Say if 8 pixels near each other were off
keep those but if they are 8 pixels randomly through the image ignore
them.
Does this sound like something logical, say using an adjacency of
pixels with a tolerance value for color and number of pixels as arguments?
See attached image for example of how little is different in the
entire image. It is a GIF zoomed in to the exact spot of 3 different
pixels so hopefully it works.
Regards,
Mathew Saunders
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