How Can Different Image Quality Metrics Be Rescaled for Uniform Comparison?

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Different image quality metrics operate on varying scales, complicating comparisons with human assessments rated from 1 to 5. To standardize these metrics, a universal scale must be chosen, such as 1-5, and other metrics can be transformed to fit this range using linear equations. The transformation involves determining the slope and intercept based on the chosen scale's endpoints. For instance, converting a scale from 0-10 to 1-5 can be achieved with the formula y = 0.4x + 1. This method allows for a consistent comparison of image quality metrics against human judgments.
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Hi to all,

my problem is the following:
I have different different quality metrics which are able to assess image quality by investigating several parameters in an image.
The problem with those metrics is their scale. Some of them range from 1 to 10 (10-highest quality) some of them from 0 to 1, etc.

I want to figure out how well those metrics correlate with assessments done by human subjects who judged the images on a scale from 1 - 5. how can I compare between the various scales? how can I rescale the mathematic metrics so that all of them range from 1 to 5? (so i can compare it to human judgement).

Thanks a lot in advance

Allen
 
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first, fix a universal metric (which is a term i just made up) that has a set range: 0-10 or 0-1 or 1-5. just pick one and then make the others conform to that via a formula that converts 0-10 to 1-5. these are all linear formulas of the form y=mx+b where x is the old scale and y is the new scale.

the case of transforming 0-10 to 1-5 or vice versa would go like this:
two points are (0,1) and (10,5). (first scale first coordinates--second scale second coordinates)

the slope is (5-1)/(10-0)=4/10=.4.
the y-intercept is 1 (this is because we're given the point (0,1)).

so the transformation is y=.4x+1.

for example, a 2 on the 1-10 scale is a .4(2)+1=1.8 on the 1-5 scale.
a 10 on the 0-10 scale is a .4(10)+1=5 of the 1-5 scale.

etc.
 

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