Scaling of measures/ metrics

  • Thread starter richterallen
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In summary, the speaker is seeking a way to compare different quality metrics that have varying scales, some ranging from 1 to 10 and others from 0 to 1. They want to find a method to rescale these metrics to a range of 1 to 5 so that they can compare them to human assessments. The suggestion is to use a universal metric with a set range and then convert the other metrics to match using a linear formula.
  • #1
richterallen
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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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  • #2
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.
 
  • #3



Hi Allen, thank you for sharing your problem with us. Scaling of measures and metrics can be challenging, especially when they have different ranges. In order to compare the different scales, you can use a statistical method called normalization. This involves transforming all the metrics to a common scale, such as a 0-1 scale, so that they can be compared. This can be achieved by dividing each metric by its maximum value and then multiplying by the desired range (in your case, 1-5). This will rescale all the metrics to the same range and make them comparable to the human judgement scale. Another approach could be to use a statistical technique called linear regression, where you can find the relationship between the different scales and use that to convert one scale to another. I hope this helps and good luck with your research!
 

1. What is scaling of measures/metrics?

Scaling of measures/metrics refers to the process of adjusting or transforming data to make it more comparable or easier to interpret. This is often used in scientific research to ensure that measurements or metrics are consistent and meaningful across different contexts or populations.

2. Why is scaling important in scientific research?

Scaling is important in scientific research because it allows for more accurate and meaningful comparisons between different data sets. It also helps to eliminate biases or differences that may arise due to variations in measurement units, sample sizes, or other factors.

3. What are some common methods of scaling measures/metrics?

There are several methods of scaling measures/metrics, including z-score transformation, min-max scaling, and standardization. Z-score transformation involves converting data to a standard normal distribution, while min-max scaling rescales data to a specific range. Standardization involves transforming data to have a mean of 0 and a standard deviation of 1.

4. How do you determine which scaling method to use?

The choice of scaling method depends on the nature of the data and the research question. For example, z-score transformation is useful for comparing data that have different units of measurement, while min-max scaling is more appropriate for data that need to be compared on a relative scale. Standardization is often used when the distribution of the data is important.

5. Are there any limitations to scaling measures/metrics?

While scaling can be a useful tool in scientific research, it is not without its limitations. Some methods may assume a normal distribution of data, which may not always be the case. Additionally, scaling can sometimes obscure important differences in the data, so it is important to carefully consider the implications of choosing a particular scaling method.

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