Color sorting using RGB, HSV, and YIQ color spaces

We recently needed to sort the pixels in an image by their color. On first attempt, we sorted by simple RGB values: first sort on R, then on G, then B. But it turns out there are many other possibilities for sorting colors. Every permutation of the RGB letters could be considered (RBG, GBR, GRB, etc.), not to mention other color systems HSV and YIQ.

To see all the color sorting possibilities, we wrote a Python script to try every permutation against a common set of source images.  Click here to see every color sorting option. Note that we use a Hilbert curve in the top set of images to provide better grouping of similar color values. The bottom set of images is linear and wrapped: left to right, then top to bottom.

Our goal was to find a sorting mechanism that looked pleasing to the eye, but of course the results are completely subjective.

• For aesthetics, we prefer the YIQ sort. It highlights trends in value change, but information about hue variation is hidden and sometimes invisible.
• To communicate more information about hues, HSV sorting is by far superior, but it lacks the pleasant gradient-effect of the YIQ sort.
• RGB sorting proves inferior to HSV and YIQ, though the results can still be interesting.

Also worth noting: HSV sort is excellent for grouping colors of similar hues, but the IYQ and YIQ sorts also do this well and they can look cleaner than their HSV counterpart.

(with thanks to Steve Witham for his Hilbert curves in Python.)

Salavon's photograph series Homes for Sale. Clockwise, from top left: Seattle, Miami, Los Angeles, New York City, Dallas, Chicago.

We find the work of Jason Salavon highly inspirational. At the intersection of art and visualization, his images are distinctive for their surface and immediate graphical language while also communicating significant stories of process and history.

By merging dozens or hundreds images that share some common theme, Salavon helps us extract high-level information about common form, texture, and color. The details are sacrificed, but we gain understanding of patterns inherent in the larger series.

In the Homes for Sale s images, aggregation is done by place and time. The photographs, all from 2002, were taken by realtors. We might expect different results in different cities, years, or seasons. As it is, we can see the green grass of Dallas contrasting with the yellow lawns in Chicago, and the blue skies of Miama and Los Angeles stand out against the gray atmospheres in Seattle and New York.

Every Playboy centerfold, by decade. From the left: 1960s, 1970s, 1980s, 1990s.

In the Playboy series, aggregation is achieved by limiting the kind of image (only centerfolds) and differentiating by decade. We see artifacts of culture in composition and colors, warm to cool, with lighter skin and hair emphasized in later decades.

Hiroshi Sugimoto, Akron Civic Theater, Ohio (1980)

We can think of Salavon’s photos as 2D films, where all frames are merged into one static image. Similar work has been done by the brilliant photographer Hiroshi Sugimoto, who’s camera has captured the entirety of feature films with one long shutter (above). Sugimoto does not wish to tell us about the film itself—in fact, the film title is never announced—but rather about qualities of light, surface, shape, form, and so forth.

Most data visualization projects start with structured data, spreadsheets, XML, databases, APIs. We like Salavon’s projects that bridge data visualization with more traditional media, using a raster imagery as the data sources, extracting patterns and meaning from highly unstructured content. They are beautiful in their own right, and serve as an inspiration for future data visualization projects.