Step 0: Need annual data, so add up the quarters. Then, apply a log transform to all the annual data converting from a multiplicative to a linear model analysis. You will also need the average of the annual logs.
Step 1: Working with just annual figures as logs, take the first difference between points. If there is only a simple underlying linear trend for the annual data this should produce a near constant differences between years (equates to a slope estimate upon, for example, 'averaging' differences). This is the Trend estimate here based from 2 differences produced from taking the difference of 3 annual figures converted to logs.
Step 2: Take second differences (the difference in the two 1st differences expressed in log values). This results here in just one number. If large then likely a Cyclical component, otherwise assume a value of zero and this is then a measure of the noise level (here referenced as the irregular component).
Step 3: Construct a fitted 3 point trend model for the log data. This annual log model is: Average of logs + Trend x time + Cyclical x time^2 where possible time values are 1, 2 and 3.
At this point, I would take the anti-log (for base 10, compute 10^(log)) for all of the fitted annual log points going back to a dollar analysis (and not the log of dollars).
Note, if there is both no Trend or Cyclical components (values are/near zero), then, the geometric mean (from the inverse of the average of the logs, the remaining term in log fit model) is the forecast for each year with a random irregular component (with an expected mean of zero).
Step 4: Compare (by dividing) say Quarter 1 to the [fitted model value (note: real dollars and not log value of dollars) for that year divided by four]. Similarly, repeat for the other quarters comparing again to respective Annual dollar Fit/4. 'Average' these respective quarter seasonal effects to arrive at your Seasonal quarter adjustment factors. Note, a functional value of 0.25 implies no seasonal effect, you are obtaining 1/4 of the annual. One could, per the cited Wikipedia reference, represent this season factor as 100 (from 400 times 0.25, and in the case of 400 times 0.30, this would be 120 implying a strong seasonal effect and to forecast the full year from this one quarter only, then multiply by 4 and divide by 1.2 as it usually over-represents the full year).
Done, you have a full Seasonal Trend Cyclical model.
Note: I have used the term average in quotes (namely 'average'), where one may decide to perform a more advanced weighting procedure to arrive at say a Trend estimate using, for example, a Least-Squares or even more advanced fitting algorithm data permitting.