interesting-reads

IBM did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For example, they found that, (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. (2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range. (3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second

Mentioned in the text is IBM, which did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For example, they found that, (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. (2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range. (3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency

> rainbow palette for use in my data visualisations. Indeed for her kind of data the rainbow palette works okay. But if you have to visualize more complex / denser data, beware of the pitfalls of the rainbow color palette. It is not always the best choice. IBM did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For example, they found that, (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. (2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range. (3) For interval and

IBM did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For example, they found that, (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. (2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range. (3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second

When I had more time (before having kids) i would print out a couple of articles every other day to read while commuting with public transport or during the evening. I became a bit obsessed with the optimal usage of paper space per article. So I wrote a HTML-to-LaTex converter as a golang learning project and applied the scientific paper style to the articles. I was quite happy with the results. Some HN classics can be found here: https://github.com/frankMilde/interesting-reads

The first link doesn't seem to have any content and the second link should be https://github.com/frankMilde/interesting-reads/blob/master/... > (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. Specifically the pdf claims that a naive rainbow color map is bad and references the work of S.S. Stevens for "hue was not a good dimension for encoding magnitude information"

Your second link is somehow broken, I think it was truncated during copy-paste. Here's the PDF link: https://github.com/frankMilde/interesting-reads/blob/master/...

For the pdf, did you mean to link to: https://github.com/frankMilde/interesting-reads/raw/master/i...

IBM did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For exampale, they found that, (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. (2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a color map which has a strong luminance variation across the data range. (3) For interval and ratio data, both luminance- and saturation-varying color maps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second

IBM did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For exmpale, they found that, (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. (2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a colormap which has a strong luminance variation across the data range. (3) For interval and ratio data, both luminance- and saturation-varying colormaps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second wi

Just in case you want a nice pdf printout read. https://github.com/frankMilde/interesting-reads/blob/master/...

Previous discussion https://news.ycombinator.com/item?id=7364442 Those who like to have a print version: https://github.com/frankMilde/interesting-reads/blob/master/...

IBM did research back in the 90s on perceptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For exmpale, they found that, (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. (2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a colormap which has a strong luminance variation across the data range. (3) For interval and ratio data, both luminance- and saturation-varying colormaps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second wi

IBM did research back in the 90s on perseptually-based colormaps and how to best represent various types of data within the color dimensions of luminescence, saturation and hue [1]. For exmpale, they found that, (1) Hue was not a good dimension for encoding magnitude information, i.e. rainbow color maps are bad. (2) The mechanisms in human vision responsible for high spatial frequency information processing are luminance channels. If the data to be represented have high spatial frequency, use a colormap which has a strong luminance variation across the data range. (3) For interval and ratio data, both luminance- and saturation-varying colormaps should produce the effect of having equal steps in data value correspond to equal perceptual steps, but the first will be most effective for high spatial frequency data variations and the second wi