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Comment by platz | Hacker News Reader

> In the case of 2D histograms the resulting plot will have the pixel intensities of a channel on the X-axis, the pixel intensities of another channel on the Y-axis, and the frequency is given by the color of the plot.

I'm still having a hard time understanding what exactly this represents and how to properly interpret it.

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There's a nice overview here: https://svi.nl/TwoChannelHistogram

I don't know any professional photographers who use 2D histograms for editing, or whether you can actually plot them in Lightroom or Photoshop. However for scientific image processing it seems to be more useful.

OpenCV also has a tutorial: https://docs.opencv.org/3.3.1/dd/d0d/tutorial_py_2d_histogra...

But which of the 'coming chapters' it's referencing I have no idea.

It shows correlations between colors. Consider two images, one with lots of purple and one with equal amounts of red and blue. Since the color purple is equal parts red and blue, we don't expect to see much difference in the one dimensional histograms between the red/blue image and the purple image. On the other hand, when we look at the 2D histogram we do find such a difference. The purple image's 2D histogram will have large values near the diaganol (red scales with blue). The red/purple image might have no correlation depending on the image, or could possibly be anti-correlated, with a diaganol as before, but with negative slope.

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