After visually analysing the 2D-scatterplot we tried to create a model for estimating the desired compression rate by aiming for the average compression rate. However, using the average as a goal for the compression estimation is not necessarily good. The average does not tell us so much about how well the compression is going to be. Therefore, instead of using an average, a wider range had to be used. Therefore, we tried to set a linear range that would be considered as the "acceptable compression range".
By using the middle line as a base, the formula then became (HF = percentage of high frequencies):
Y = -2/5 ∗ HF + 80 (the middle line created from Y = kx + m).
This formula was then used to estimate the close to optimal compression rate for the remaining 20 images. The results were then compared against our current compression algorithm on TinyJPEG to measure how satisfied the users were. From the results it was clear that the new estimation formula would have slightly more satisfied users.
Based on the experiments and the results we can say that it is possible to measure different characteristics of an image to get an estimation of how much an image can be compressed. The characteristics play a big part of how well an image can be compressed, however, there was only one of the chosen characteristics that had any real significance and that was the percentage of high frequencies.
The main problem with the current estimation formula is that there is no clear connection to why some images could not be properly estimated. If we want to create an algorithm for finding the absolute best possible compression rate in the future then we would most likely have to conduct more experiments including other characteristics.
I do believe that this research could have laid the groundwork for a new way of finding the best possible compression rate. By using the technique presented in this research not only would the current process be faster but also more accurate since we are compressing each image based on their own characteristics.
Interested in reading more? Have a look at my thesis.