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I thought a bit since I read the article, and thought of ways to improve the results:
Not increasing the intensity of the background color. On a few example pictures the background becomes garish and over-saturated.
Using the CIE color space for clustering. It is much more accurate for calculating perceived color distance, and will result in superior clustering. CIE is implemented by the colormath library.
Using a walking median to calculate the background color. Could be done individually by color. Would result in a background color not stripped of the least significant bits.
The text was updated successfully, but these errors were encountered:
I thought a bit since I read the article, and thought of ways to improve the results:
Not increasing the intensity of the background color. On a few example pictures the background becomes garish and over-saturated.
Using the CIE color space for clustering. It is much more accurate for calculating perceived color distance, and will result in superior clustering. CIE is implemented by the colormath library.
Using a walking median to calculate the background color. Could be done individually by color. Would result in a background color not stripped of the least significant bits.
The text was updated successfully, but these errors were encountered: