I found it interesting that:
– Producing colors with spectrum identical to the LMS sensitivity curves is required for a display device to create all perceivable colors.

Something I couldn’t figure out:
– In Stone, simultaneous contrast occurs because of differing white among cones.
– In Ware, simultaneous contrast occurs in or after V1.
– If Stone is correct, then cones from different regions of the retina have different whites. (Otherwise, the contrast illusion cannot appear.) This is interesting because cones also have a global white – if a bright point light occurs in darkness, the entire eye is desensitized.

Although, many of these color principle are helpful in all sorts of visualization, I was particularly thinking about interactive plots with large number of variables. The emphasis and de-emphasis properties of color (e.g. page 78) can be interactively manipulated to help with visual queries.

From Stone: I haven’t fully grasp some of the concepts, related to generation of color , presented in this paper. I am still little confuse about what the author meant by multiple spectra. I guess I never studied color in this detail before. I will make another attempt before the class to understand the paper.

Perhaps the most important thing that I learned from this chapter was the importance of “Luminance Channel” in understanding motion, stereoscopic depth, and shapes from shading. Also, whenever detailed information is to be shown, luminance contrast
is important which is critical for small text.

One important suggestion that this chapter makes about presenting zero in data analysis. In order to make zero intuitive, a neutral value of red-green and yellow-blue channel is a good choice.

This chapter has shown good pictures to convey the fact that colors can totally destroy our ability to see the shape of the surface.

It is interesting that many great photographers, movie makes and artists still favor black and white over other colors.

Representing Colors as Three Numbers:
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This paper was very difficult to read and extract useful information. I failed to understand the objectives and conclusions of this paper.

Another thing that had me a little confused was the Purple Line — mentioned both in the old Ware and Stone. Why does our color space have a straight line between R and B? Is it just that there needs to be a straight line between two primaries in that style of representation of color space, or is there something special about Red and Blue?

The rest of the readings were neat and straightforward — it’s nice to finally see why blue text on black is so hard to read; also, the illustrations of contrasts only in hue are delightfully frustrating to read, reminding me of many a bad powerpoint presentation.

Also: I wish I could see in 12-color Chicken Vision.

Ware claims that “the three-dimensional nature of color space is a … consequence of having three cone types in the retina”, then writes in the next paragraph that color vision is three dimensional because “it only takes a combination of three lights to create a full range of colors.” The relationship between these two causes isn’t obvious until reading about the CIE’s color matching experiments, and that cone response functions are linear mappings of color-matching functions.

Stones paper is really a good compensation for Colin Ware’s Chapter 4. I really enjoyed Chapter 4 since I can understand most part of it. The “Watchband” style diagram of hue saturation (darker and lighter means less saturation) and the difference between luminance and chromatic channels are really new to me, and provide useful design criterias for my future design. The shape from shading and contrast part also gives great thought, but I think these can be used as design methods equally important to the color coding.

I also liked the rather short part where he talks about the semantics of color(for example, in Korea it is considered rude – almost a curse – to write other people’s name in red!). It reminded me also of the color chapter of the book ‘Understanding Comics’, where the author says that choosing a specific color palette alone can even link to specific superhero characters. In this way, the semantics of color could be useful in doing a more rhetorical storytelling.

Stone’s paper explains that our tri-color vision is non-linear while the RGB coding system is. So she proposes a new formular to make it correspond better and reduce color perception errors. I had a hard time to grasp the concepts within and am had an even harder time to draw implications for designing data visualizations. Maybe it connects to choosing the right color palette to maximize luminance contrasts.

Across both readings, however, this idea of negative light appears. Both readings contain a great deal of information as far as the mathematical foundations of current color systems; however, all of these systems seem to operate on the fundamental principle that negative color is attainable. This fact is actually very rapidly disproven in Ware’s book, suggesting that the very mathematical frameworks that the aforementioned uniform color spaces lie in may be a mathematic impossibility. Thus, the question remains, which is more relevant for color analysis: mathematical frameworks or perceptual experimentation.

All uncertainty aside, however, Stone continually stresses the importance of the linear and nonlinear natures of color computation with respect to color matching and selection. Not only do these properties appear important to the accuracy of the calculations, they also bring to the forefront of the color argument which color space is more effective: that based on a linear system or that of a nonlinear perceptual system. After completing both readings, I would argue that perceptual definitions of color are more significant with regard to color selection. While uniform color spaces play nicely in the theoretical realm, it feels as if perception tends to avoid such logical simplicities. For instance, the fact that a white-black color ramp essentially only has 4 perceptually significant encodings, despite covering a maximal distance in uniform color systems, seems to imply that it is what we see which is the more important consideration in color selection that what seems computationally correct. Given that visualizations are designed to exploit the efficiency of the perceptual system, it would only make sense that colors be selected to play off of this theme.

As an exercise in my undergraduate graphics class, we went online to a rgb triangle to see how much of each was needed to create what appeared to be white on the screen, which unsurprisingly, required a lot less green than other colors, which does make sense evolutionarily.

Having studied color from a theatre lighting design viewpoint, it’s interesting to see it in a different way. When doing theatre, we focused primarily on what color combinations did and how combining colors in light differ from combining color in paint. In these readings, the focus seems to be in comparing between two colors themselves and how luminosity and other aspects affect those differences. This makes sense, as most visualizations will likely involve comparisons.