Monday, November 10, 2014

Orange rage against the dying of the light


It’s autumn again, and the sunlight fades. The world waxes orange, from tree leaves to squash and pumpkins (and their Halloween simulacra). Last autumn’s ISCC News had a Hue Angles column on the color orange. Now let’s look at a possible psychological effect of the color: its hypothetical ability to encourage a fighting response---even rage (as if orange in autumn encourages us to "rage against the dying of the light," to quote Dylan Thomas).

Football teams such as that of Syracuse University sport orange uniforms. Could this be a kind of visual “fight song?” I don’t know about Syracuse, but it surely was a visual “fight song” for Princeton’s baseball team, which long ago adopted the colors orange and black based on such a premise. This is recalled by one of us (HSF) with particular authority: His father was the head of the Princeton athletic department.

Here are the relevant facts [see ref. 1]: In May of 1869, the Princeton College Class of 1869 Baseball Club had a game against Yale scheduled to take place in New Haven. As visitors they would traditionally wear black shirts and black or grey pants. The pants were likely their street clothes as were their shoes. The home team would wear white shirts and the same selection of pants. The club, hearing that orange was a color that inspired fear in an opponent, elected to have about an inch and a half ribbon commissioned that said “ ’69 B.B.C” in black on a bright orange ribbon. They cut these to length and wore them on their arms the way one would wear a black ribbon at a funeral. This was the first recorded use of orange and black among Princeton teams.The first college football game would not be played until the fall of the next academic year, November 1869. Princeton participated in that event, but there is no record as to whether they wore orange that day. By the 1880’s Princeton football teams were wearing black jerseys with orange and black stripes around the sleeves. 

Odeda Rosenthal, who is best known in the color world for her book Coping with Color Blindness [Avery Pub. Grp., 1997], once presented an ISCC paper (probably in Interest Group III, in the late 1990s) on the relation of orange color and rage. Nobody seems able to find an abstract, but a personal letter she sent to me 25 Oct 1997 made the promise, “Next year I would like to present OBSERVATIONS ON THE COLOR ORANGE. By then I should be in the green.” I remember hearing the presentation---not deep and scholarly, but anecdotal and forceful.  She was wearing an orange dress. Cynthia Sturke remembers this too---I am not alone in my recollection.

Although the evidence is circumstantial, one of us (MHB) couldn’t resist using the message of the paper. When he went to Tambov in 2008 to teach English to Russian students, he wrote this poem to be read at the closing ceremony, after he saw the  commemorative  T-shirts in the terrible color scheme of bright orange, white, and beige.

Color Selection

The RGBs of color---
the laws that give us light
say: Don’t prepare your briefing slides
in colors equi-bright.

Though op art thrived on reds and greens
of equal luminosity,
attempts to read such art
could cause blown lunch of low viscosity.

The hues themselves are also key:
A certain study shows
bright orange taps amygdalas
and makes us come to blows.

So make your message clearly seen
in hues that don’t enrage:
Don’t print it on a T-shirt
in bright orange, white, and beige.

Are Princeton’s visual fight song, Odeda’s paper, the colors of autumn, and Dylan Thomas all connected? Odeda would perhaps have speculated, but sadly she is not with us, having passed away more than six years ago.

1. Don Oberdorfer, Princeton University: The First 250 Years, ISBN 0-691-01122-2, The Trustees of Princeton University (1995), pg. 68.

Michael H. Brill and Hugh S. Fairman

Monday, August 4, 2014

Battle of the senses

Rolf Kuehni just alerted me to an article in Science [1] that says we perceive a trillion different odors. To compute such a limit, one must count just-noticeable differences (JNDs) within a physically constrained stimulus space, as is familiar for color discrimination. This led me to ask two questions. First, how does this number compare with the number of distinct odors sensed by, say, a grizzly bear, whose sense of smell is said to be 100,000 times that of humans [2]? Secondly, how does this number compare with the number of colors sensed by a human being?

Grizzly bear: From

I’ll attempt to answer the second of these questions first. Since most of us consider vision to be our primary sense, the comparison with olfaction is distressing, and perhaps a bit controversial. Despite the fact that some display mavens say we see 16 million colors (on the basis of eight bits times three channels of color), cynics add “most of which are black.” The problem of counting discriminable object colors is a bit tricky, as many have noted. In general one counts colors by finding a perceptually uniform color space, delimiting the object-color solid in this space, and finding the volume of the object-color solid as the number of 1-JND cubes that can be packed therein. One number that seems to bear some authority is 2 million colors [3], but note that even the title of the article [3] attests to the trickiness of the assessment. For one thing the assessment is done in CIECAM02 space, which is arguably not perceptually uniform.
The number 2 million is paltry compared with a trillion, so either we are under-using our noses or we are counting wrong. I subscribe to the latter idea. When comparing the senses, you have to make sure you are counting the same kind of thing. For one thing we have binocular vision, but when you denote a trillion odors, you haven’t got other odor sensors that give you other dimensions (such as, say, two noses that give “bi-nasal olfaction”). More importantly, spatial vision has no counterpart in olfaction. So to be fairer to vision, you have to multiply the number of object colors (say 2 million) by the number of LMS cone triplets, which is about 2 million [4]. This may give too much advantage to vision, because the number of fibers in the optic nerve is only about 1 million [5], which reduces the spatial address number to only about 300,000. Assuming each of these spatial addresses sees 2 million possible colors, the estimated total number of visual inputs is 600 billion---comparable to olfaction, but not exceeding it.
It appears that vision loses to olfaction by more than just “a nose”. And (returning to the first question) beware of the grizzly bear.


1. Bushdid, C, Magnasco, MO, Vosshall, LB, Keller, A. Humans can discriminate more than 1 trillion olfactory stimuli. Science 21, 343, 6177 pp 1370-72 (March 21, 2014). 

2., accessed 27 Jun 2014.

3. Masaoka, K, Berns, RS, Fairchild, MD, Abed, FM. Number of discernible object colors is a conundrum. Journal of the Optical Society A 30 (2) 264-277 (2013).

4., accessed 27 Jun 2014.

5., accessed 27 Jun 2014.

Michael H. Brill

Wednesday, April 30, 2014

Blue snow, eternal winter

The Blue Ice of Antarctica, photo by Brendan van Son [1]

In my neighborhood, a clean and newly fallen snow (one of many this winter) showed a blue color in its concavities---an effect similar to the more grandiose photo above. The sky was gray---as in this photo---so it couldn’t be a reflection of the blue of the sky. Furthermore, although I had seen plenty of photographs of blue-looking snow and dismissed them as artifact, this time my direct view denied that excuse.

Why was the snow blue? The main mechanism of the blueness is water’s absorption of long-wavelength visible light, leaving the short-wavelength end of the spectrum to reflect to a viewer.  Incident light finds a long path length through the snow (or ice), followed by deep scattering from air bubbles. The concavities intensify the blueness through multiple reflections.

Water’s absorption of long-wavelength light is familiar to scuba divers, and is responsible for the success of blue-green lasers for undersea communication: red lasers don’t penetrate very far into the water.

Seeing the blue snow reminded me of a remark I made about C. V. Raman in an earlier Hue Angles [2]: “A 1921 trip returning to India from England made him marvel at the blue of the ocean, and to posit that blue as arising from molecular scattering of light by water molecules, not just reflection of the blue of the sky (as Lord Rayleigh supposed).” Soon after his return, Raman wrote a long paper for the Royal Society that said the sea is blue because of the same kind of scatter as Rayleigh attributed to the sky---so the sea generates its own Rayleigh-type scattering [3, p. 35; 4].

To me, the clarity of images seen through even a large path-length of clean water seems inconsistent with scatter---which incurs haze due to random change of direction of the light. Is the blue of the ice cliff and of my small snow-dimples due to molecular scatter per Raman’s thinking in 1922? I don’t think so.

Raman’s fascination with ocean color ultimately led to his receiving the Nobel Prize for discovering the Raman effect. Can the blue of water or snow be explained by the Raman effect? Again, no---and I think he would have agreed. The Raman effect is much weaker than the blue light from the ice (only one in ten million photons is Raman-scattered, which is even less than the one in ten thousand for Rayleigh scattering from the sky) [5].

Given the weakness of the Raman effect, how can it be observed at all? One needs two tricks.

First, to eliminate the reflected light, illuminate a sample with an excitation wavelength and observe it at a somewhat longer emission wavelength. Raman used the 435.8 nm line of a mercury lamp (with other lines filtered out) as an excitation, and then removed the 435.8 nm line with a further filter after the light had interacted with the sample. What was left was the small portion of the light whose wavelength was altered by the sample. Getting the right filters to do this was not easy, especially in Raman’s time and place.

Secondly, to eliminate fluorescence as an explanation, observe whether the wavelength-altered light is polarized. Fluorescence comprises absorption and re-radiation of light, with no memory of the geometry of the incident light.  But Raman-scattered light is polarized [3, 6] so the electric field is perpendicular to the plane of the incident ray, scatterer, and detector.   Furthermore, unlike in fluorescence, the wavenumber (or frequency) shift of Raman-scattered light is independent of the excitation wavelength [5].

So, you can see that the blue color of water, ice and snow inspired much science and some ideas that even now are open to debate. Seven busy years separated Raman’s marveling at the blueness of the ocean and his discovery (1928) of the effect that bears his name.  I recommend Venkataraman’s book [3] for a chronicle of those years.

Returning to the seemingly eternal winter of 2014, I hope that by the time you read this column you will be contemplating the greens and yellows of spring.

1.; for other photos see
2. M. H. Brill, C.V. Raman’s explorations in color science, ISCC News # 441, Sep-Oct 2009, pp. 3-4.
3. G. Venkataraman, Raman and his Effect by (Universities Press, 1995, reprinted 2009).
4. C. V. Raman, On the molecular scattering of light in water and the colour of the sea. Proc. R. Soc. Lond. A 101 (1922), 64-80. [see]
6. C. V. Raman and K. S. Krishnan. A new type of secondary radiation. Nature (London) 121, 501-502 (1928). [see]

Michael H. Brill
(6 March 2014)

Monday, February 3, 2014

Seeing Brightness in the Season of Little Light

 FIGURE: Robert Pepperell’s “Christmas Scene.” Reprinted with permission of the artist.

 I am about to talk about how we experience brightness as we struggle with the winter solstice.

Brightness is an attribute of sensation of light. It is hard to pin down, partly because it is easily confused with luminance (whose equalization across a visual boundary makes the boundary minimally distinct) and with lightness (the apparent reflectance of a non-self-luminous body).

A light’s spectral content contributes to its brightness, but is not the whole story. To show that brightness doesn’t necessarily increase with light intensity, Bill Thornton used to project three circles of light (red, green, and blue) on a screen, in such a way that the areas underwent mutual overlap. In his demonstrations, the whitish center (with all beams present) was invariably the least bright, and the separate red and green areas appeared the brightest.

Psychophysicists and artists have long acknowledged the contribution to brightness of geometric factors that are at least as influential as the spectral content.  Ernst Mach (from whom Mach bands are named) observed that a visible region appears brighter when it is next to a darker region. Mach’s phenomenon is clear when the gray screen of a turned-off TV set develops black regions when the set is turned on.

Truly, the brightness of a light depends on its spectral content and on contrast with its neighbors, but even this is not the whole story. The influences I described above can be incorporated into a vision theory that has three kinds of receptors (cones) and comparisons of spatially adjacent visual inputs, but that is just low-level vision. High-level vision---which involves recognition of whole objects and their spatial context---may also enter. Over the years we have seen some striking demonstrations of global influences in the visual field (the work of Alan Gilchrist comes to mind), but I have noticed an effect that is not widely noted. I call it the “Christmas-tree-light effect”, because I first had my attention drawn to it by Robert Pepperell’s painting “Christmas Scene”.

I met Pepperell and his painting at the 2012 SPIE Electronic Imaging Symposium, a conference at which I always seek the explanation of “new” visual effects by old theories. Accordingly, looking at the “Christmas Scene”, I was surprised to see that the Christmas-tree lights were blurred and yet seemed brighter than they might have appeared otherwise. Classical simultaneous-contrast models would have predicted a diminution of brightness by blurring the lights, but it seemed the opposite was true. You can see the Christmas-tree-light effect in the figure here because Robert Pepperell has graciously given permission for me to reprint it.

At this moment you may remark (as our ISCC News editor did) that my assertion of enhanced brightness is not quite supported by Pepperell’s painting. Indeed, this objection is correct: I have not controlled the visual experiment by, say, Photoshopping all the tree lights to remove their blur patterns. Besides the obvious explanations of personal laziness and reluctance to upset the artist with my experiment, I respond to the objection by saying that enhancement of brightness by blur has a sound ecological story. Also, this kind of trick has been used by photographers for a long time.

Why should high-level visual processing interpret a blur around a light as evidence that the light is very bright? Well, any light seen through the eye is surrounded by blur due to light scattering by the eye. The blur is invisible if the light is very dim, but it becomes more visible as the light becomes more intense. In a dark living room, a point of light from (say) an LED is very intense, and saturates (dazzles) the visual response. The only remaining evidence of the light’s brightness will then be the blur, which (as a fixed fraction of the intensity of the light) will be quite visible.

And what is the photographic trick that uses the “Christmas-tree-light” effect? It is  called the “star effect” [1], and involves turning each point of light into a star-shaped pattern using a periodically ruled grid called a star filter.  There’s some nice Fourier-transform theory here, as well as good optics. But for purposes of the present essay, it is merely further evidence that points of light can be “enlivened” (which I interpret to mean, “enhanced in apparent brightness”) by surrounding them with patterns of lower intensity.

A parting problem: How is it that I see star patterns around points of light at night? Surely I don’t have a ruled grid in my eye that piles up Fourier components of light in an orderly way…

1. Tiffen, I., Star effects: enliven points of light – how star filters work. Print edition: Student Filmmakers Magazine, June 2008, p. 44; online edition at

Michael H. Brill