I was curious to see how all these colormaps fared, but my expectation was that Jet would sink to the bottom. I was really surprised to see it came on top, one vote ahead of the linear lightness rainbow (21 and 20 votes out of 62, respectively). The modified heated body followed with 11 votes.
My surprise comes from the fact that Jet carries perceptual artifacts within the progression of colours (see for example this post). One way to demonstrate these artifacts is to convert the 2D map into a 3D surface where again we use Jet to colour amplitude values, but we use the intensities from the 2D map for the elevation. This can be done for example using the Interactive 3D Surface Plot plugin for ImageJ (as in my previous post Lending you a hand with image processing – introduction to ImageJ). The resulting surface is shown in Figure 1. This is almost exactly what your brain would do when you look at the 2D map colored with Jet in the previous post.
Figure 1
In Figure 2 the same data is now displayed as a surface where amplitude values were used for the elevation, with a very light sun shading to help a bit with the perception of relief, but no colormap at all. to When comparing Figure 1 with Figure 2 one of the artifacts is immediately recognized: the highest values in Figure 2, which honours the data, become a relative low in Figure 1. This is because red has lower intensity than yellow and therefore data colored in red in 2D are plotted at a lower elevation than data colored in yellow, even though the amplitudes of the latter were lowest.
Figure 2
For these reasons, I did not expect Jet to be the top pick. On the other hand, I think Jet is perhaps favoured because with consistent use, our brain, learns in part to accommodate for these non-perceptual artifacts in 2D maps, and because it has at least two regions of higher contrast (higher magnitude gradient) than other colormaps. Unfortunately, as I wrote in a recently published tutorial, these regions are randomly placed in the colormap, and the gradients are variable, so we gain on contrast but lose on faithfulness in representing the data structure.
Matt Hall wrote a great comment following the previous post, really making an argument for switching between multiple colormaps in the interpretation stage to explore and highlight features in both the signal and the noise in the data, and that perhaps no single colormap is best overall. I agree 100% on almost everything Matt said, except perhaps on the best overall: looking at the 2D maps, at least with this dataset, I feel the heated body could be the best overall colormap, even if marginally. In Figure 3, Figure 4, Figure 5, and Figure 6 I show the 3D displays obtained by converting the 2D grayscale, linear lightness rainbow, modified heated body, and cube llightness rainbow, respectively. Looking at the 3D displays altogether gives me a confirmation of that feeling.
Steve Eddins of the Matwork just published a post announcing a new Matlab colormap to replace Jet. It is called Parula (more to come on his blog about this intriguing name).
First impression: Parula looks good.
And while I haven’t had time to take it into Python to run a full perceptual test and into ImageJ for a colour blindness test, as a preliminary test I did convert it to grayscale with an online picture converting tool that uses the lightness information to perform the conversion (instad of just desaturating the colors) and the result shows monotonic changes in gray.
Since then Giuliano has been kind enough to provide me with the data for one of his spectrograms, so I am resuming the discussion. Below here is a set of 5 figures generated in Matlab from the same data using different colormaps. With this post I’d like to get readers involved and ask to cast your vote for the colormap you prefer, and even drop a line in the comments section to tell us the reason for your preference.
In the second post I’ll show the data displayed with the same 5 colormaps but using a different type of visualization, which will reveal what our brain is doing with the colours (without our full knowledge and consent), and then I will ask again to vote for your favourite.
Perceptual rainbow palette – Matlab function and ASCII files
In my last post I introduced cubeYF, my custom-made perceptual lightness rainbow palette. As promised there, I am sharing the palette with today’s post. For the Matlab users, cube YF, along with the other palettes I introduced in the series, is part of the Matlab File Exchange submission Perceptually improved colormaps.
For the non-Matlab users, please download the cubeYF here (RGB, 256 samples). You may also be interested in cube1, which has a slightly superior visual hue contrast, due to the addition of a red-like color at the high lightness end but at the cost of a modest deviation from 100% perceptual. I used cube 1 in my Visualization tips for geoscientists series.
Perceptual rainbow palette – preformatted in various software formats
The palettes are also formatted for a number of platforms and software products: Geosoft, Hampson-Russell, SMT Kingdom, Landmark Decision Space Geoscience, Madagascar, OpendTect, Python/Matplotlib, Schlumberger Petrel, Seisware, Golden Software Surfer, Paradigm Voxelgeo. Please download them from my Color Palettes page and follow instructions therein.
Another example
In Comparing color palettes I used a map of South America [1] to compare a linear lightness palette to some common rainbow palettes using grayscale as a perceptual benchmark. Below, I am doing the same for the cubeYF colormap.
Comparison of South America maps using, from left to right: ROYGBIV (from this post) , classic rainbow, cubeYF, and grayscale
Again, there is little doubt in my mind that cubeYF does a superior job compared to the other two rainbow palettes as it is free of artefacts [2] and more similar to grayscale (with the additional benefit of color).
The ROYGBIV and cubeYF map have been included in Marek Kultys’ excellent tutorial Visual Alpha-Beta-Gamma: Rudiments of Visual Design for Data Explorers, recently published on Parsons Journal for information mapping, Volume V, Issue 1.
An online palette testing tool
Both cubeYF and cube1 feature in the colormap evaluation tool by the Data Analysis and Assessment Center at the Engineer Research and Development Center. If you want to quickly evaluate a number of palettes, this is the right tool. The tool has a collection of many palettes, organized by categories, which can be used on 5 different test image, and examined in terms of RGB components and human perception. Below here is an example using cube YF.
An idea for a palette’s mood test
A few weeks ago, thanks to Matt Hall (@kwinkunks on twitter), I discovered Colour monitor, a great online tool by Richard Weeler (@Zephyris on twitter). You supply an image; Colour monitor analyses its colors in terms of hue, saturation and luminance and produces a graphical representation of the image’s mood [3]. I thought, what a wonderful idea!
Then I wondered: what if I used this to tell me something about a color palette’s mood? The circular histogram of colors reminded me of the Harmonic templates [4] on the hue wheel from this paper And so I created fat colorbars using the three palettes I used in the last post, saved them as images, and run the monitor with them. Here below are the results for Matlab jet, Industry Spectrum, and cubeYF. Looking at these palettes in terms of harmony I would say that jet is not very harmonic (too large a portion of the hue circle; the T template, which is the largest, spans 180 degrees), and that the spectrum is terrible.
CubeYF is also exceeding a bit 180 degrees, but looks very close to a T template rotated by 180 degrees (rotations are allowed). So perhaps I could trim it a bit? But to me it looks a lot nicer and gives me a vibe of really good mood, and reminds me of one of those beautiful central american headdresses, like Moctezuma’s crown.
[2] Looking at the intensity of the colorbars may help in the assessment: the third and fourth colorbars are very similar and both look perceptually linear, whereas the first and second do not.
[3] Quoted from Richard’s blog post: “… in the middle is a circular histogram of the colours (spectral shades) in the image, and gives an idea of how much of each colour there is. Up the left is a histogram of image brightness (lightness of colour), and up the right is a histogram of colour saturation (vibrancy)”.
[4] Quoted from the paper’s abstract: “Harmonic colors are sets of colors that are aesthetically pleasing in terms of human visual perception. If you are interested in this idea there is a set of slides and a video on the author’s website
In my last post I introduced a CIE Lab linear L* rainbow palette from a paper by Kindlmann et al. [1]. I used this palette with a map of South America created with data from the Global Land One-km Base Elevation Project at the National Geophysical Data Center. The map is the third one in the figure below.
Based on visual inspection I argued that linear L* colored map compares more favourably with the grayscale – my perceptual benchmark – on the right – than the first and second, which use my ROYGBIV rainbow palette (from this post) and a classic rainbow palette, respectively. I noted that looking at the intensity of the colorbars may help in the assessment: the third and fourth colorbars are very similar and both look perceptually linear, whereas the first and second do not.
So it seems that among the three color palettes the third ones is the best, but…..
… prove it!
All the above is fine and reasonable, and yet it is still very much subjective. How can I prove it, convince myself this is indeed the case?
Well, of course one way is to use my L* profile and Great Pyramid tests with Matlab code from the first post of this series. Look at the two figures below: comparison of the lightness L* plots clearly shows the linear L* palette is far more perceptual than the ROYGBIV.
One disadvantage of this method is that you have to use Matlab, which is neither free nor cheap, and have to be comfortable with some code and ASCII file manipulation.
Just recently I had an idea for an open source alternative with ImageJ and the 3D color inspector plugin. The only preparatory step required is to save a palette colorbar as a raster image. Then open the image in ImageJ, run the plugin and display the colorbar in Lab space in a 3D view. There are many options to change the scale of the plot, the perspective, and how the colors are displayed (e.g. frequency weighted, median cut, etcetera). The view can be rotated manually, and also automatically. Below I am showing the rotating animations for the same two palettes.
Discussion
The whole process, including the recording of the animations using the Quicktime screencast feature, took me less than 10 minutes, and it leaves no doubt as to which one is the best color palette. Let me know what you think.
A few observations: in 3D the ROYGBIV palette is even more strikingly and obviously non-monotonic. The lightness gradient varies in magnitude, resulting in non-uniform contrast. Compare for example the portion between blue and green to that between green and yellow: these have approximately the same number of samples but very different change in lightness value between the extremes. The gradient sign also changes, producing perceptual inversions, for example with the yellow to red section following the blue to yellow. These inversions may result in perceived elevation inversions, for example, if using this palette to display elevation data. On the other hand, the linear L* palette nicely spirals upwards with L* changing monotonically from 0 to 100.
After my previous post in this series there was a great discussion on perceptual color palettes with some members of the Worldwide Geophysicists group on LinkedIn. Ian MacLeod shared some really good examples, and uploaded it in here.
HSL linear L rainbow palette
Today I’d like to share a color palette that I really like:
It is one of the palettes introduced in a paper by Kindlmann et al. [1]. The authors created their palettes with a technique they call luminance controlled interpolation. They explain it in this online presentation. However they used different palettes (their isoluminant rainbow, and their heated body) so if you find it confusing I recommend you look at the paper first. Indeed, this is a good read if you are interested in colormap generation techniques; it is one of the papers that encouraged me to develop the methodology for my cube law rainbow, which I will introduce in an upcoming post.
This is how I understand their method to create the palette: they mapped six pure-hue rainbow colors (magenta, blue, cyan, green, yellow, and red) in HSL space, and adjusted the Luminance by changing the HSL Lightness value to ‘match’ that of six control points evenly spaced along the gray scale palette. After that, they interpolated linearly along the L axis between 0 and 1 using the equation presented in the paper.
CIE Lab linear L* rainbow palette
For this post I will try to create a similar palette. In fact, initially I was thinking of just replicating it, so I imported the palette as a screen capture image into Matlab, reduced it to a 256×3 RGB colormap matrix, and converted RGB values to Lab to check its linearity in lightness. Below I am showing the lightness profile, colored by value of L*, and the Great Pyramid of Giza – my usual test surface – also colored by L* (notice I changed the X axis of both L* plots from sample number to Pyramid elevation to facilitate comparison of the two figures).
Clearly, although the original palette was constructed to be perceptually linear, it is not linear following my import. Notice in particular the notch in the profile in the blue area, at approximately 100 m elevation. This artifact is also visible as a flat-looking blue band in the pyramid.
I have to confess I am not too sure why the palette has this peculiar lightness profile. I suspect this may be because their palette is by construction device dependent (see the paper) so that when I took the screen capture on my monitor I introduced the artifacts.
The only way to know for sure would be to use their software to create the palette, or alternatively write the equation from the paper into Matlab code and create a palette calibrated on my monitor, then compare it to the screen captured one. Perhaps one day I will find the time to do it but having developed my own method to create a perceptual palette my interest in this one became just practical: I wanted to get on with it and use it.
Fixing and testing the palette
Regardless of what the cause might be for this nonlinear L* profile, I decide to fix it and I did it by simply replacing the original profile with a new one, linearly changing between 0.0 and 1.0. Below I am showing the L* plot for this adjusted palette, and the Great Pyramid of Giza, both again colored by value of L*.
The pyramid with the adjusted palette seems better: the blue band is gone, and it looks great. I am ready to try it on a more complex surface. For that I have chosen the digital elevation data for South America available online through the Global Land One-km Base Elevation Project at the National Geophysical Data Center. To load and display the data in Matlab I used the first code snippet in Steve Eddin’s post on the US continental divide (modified for South America data tiles). Below is the data mapped using the adjusted palette. I really like the result: it’s smooth and it looks right.
But how do I know, really? I mean, once I move away from my perfectly flat pyramid surface, how do I know what to expect, or not expect? In other words, how would I know if an edge I see on the map above is an artifact, or worse, that the palette is not obscuring real edges?
In some cases the answer is simple. Let’s take a look at the four versions of the map in my last figure. The first on the left was generated using th ROYGBIV palette I described in this post. It would be obvious to me, even if I never looked at the L* profile, that the blue areas are darker than the purple areas, giving the map a sort of inverted image look.
But how about the second map from the left? For this I used the default rainbow from a popular mapping program. This does not look too bad at first sight. Yes, the yellow is perceived as a bright, sharp edge, and we now know why that is, but other than that it would be hard to tell if there are artifacts. After a second look the whole area away from the Andes is a bit too uniform.
A good way to assess these maps is to use grayscale, which we know is a good perceptual option, as a benchmark. This is the last map on the right. The third map of South America was coloured using my adjusted linear L* palette. This maps looks more similar to our grayscale benchmark. Comparison of the colorbars will also help: the third and fourth are very similar and both look perceptually linear, whereas the third does show flatness in the blue and green areas.
Let me know what you think of these examples. And as usual, you are welcome to use the palette in your work. You can download it here.
UPDATE
With my following post, Comparing color palettes, I introduced my new method to compare palettes with ImageJ and the 3D color inspector plugin. Here below are the recorded 3D animations of the initial and adjusted palettes respectively. In 3D it is easier to see there is an area of flat L* between the dark purple and dark blue in the initial color palette. The adjusted color palette instead monotonically spirals upwards.
In my last post I discussed the two main issues with the rainbow color palette from the point of view of human color vision, and concluded one of these issues is insurmountable.
But before I move to presenting alternative color palettes, let me give you one last example of how bad the rainbow is. It was sent to me by Antony Price, a member of the LinkedIn group Worldwide Geophysicists. Antony created a grayscale and a rainbow-colored version – using the same data range and number of intervals – of the satellite altimeter derived free-air gravity map of the world [1]. I am showing the two maps below.
Following the first post in this series, Steve commented:
Matteo, so would I be correct in assuming that the false structures that we see in the rainbow palette are caused by inflection points in the brightness? I always assumed that the lineations we pick out are caused by our flawed color perception but it looks from your examples that they are occurring where brightness changes slope. Interesting.
As I mention in my brief reply to the reader’s comment, I’ve done some reading and more experiments to try to understand better the reasons behind the artifacts in the rainbow, and I am happy to share my conclusions. This is also a perfect lead into the rest of the series.
Human vision vs. the rainbow – issue number 1
I think there are two issues that make us see the rainbow the way we see it; they are connected but more easily examined separately. The first one is that we humans perceive some colors as lighter (for example green) and some as darker (for example blue) at a given light level, which is because of the difference in the fundamental color response of the human eye for red, green, and blue (the curves describing the responses are called discrimination curves).
There is a well written explanation for the phenomenon on this website (and you can find here color matching functions similar to those used there to create the diagram). The difference in the sensitivity of our cones explains why in the ROYGBIV color palette (from the second post in this series) the violet and blue appear to us darker than red, and red in turn darker than green and yellow. The principle … applies also to mixes involving the various cones (colours), hence the natural brightness of yellow which stimulates the two most reactive sets of cones in the eye. We could call this a flaw in color perception (I am not certain of what the evolutionary advantage might be), which is responsible for the erratic appearance of the lightness (L*) plot for the palette shown below (If you would like to know more about this plot and get the code to make it to evaluate color palettes, please read the first post in this series).
So to answer Steve, I think yes, the lineations we pick in the rainbow are caused by inflection points in the lightness profile, but those in turn are caused by the differences in color responses of our cones. But there’s more!
