The Shining Montage

The Shining Montage

20224 frames from Stanley Kubrick’s 1980 film, arranged chronologically from left to right and top to bottom

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Eyes Wide Shut Graph

Image

This is my first attempt at mapping frames onto an x-y axis. In this case, the x axis is median hue while the y axis is median saturation; all values were determined by Software Studies’s ImagePlot macro for ImageJ. I think the main limitation of this method is the sheer quantity of images I’m graphing. As you can see from my earlier post, the vast majority of frames in Eyes Wide Shut seem to be composed of very warm yellow colors; in this visualization, such frames would appear on the far left of the graph. However, since ImageJ processes the images in numerical order, chronologically early frame are often covered over by later ones. If you look closely, for instance, you can see that every frame from the ballroom scene has been obscured by later scenes (mostly the prostitute scene and the bedroom argument). The solution is obviously fewer images, but I’m not sure if I should simply decimate them or try to pick out representation frames from each shot.

Aside from that, I feel like this initial result is really promising. The graph really lets you visualize the extreme distance between Bill’s domestic life and his nocturnal wanderings, epitomized by the orgy scene. You can actually see the progression starting at the warm yellows of incandescent light bulbs and soft Christmas lights which illuminate Bill’s apartment. Next, the deep blues which characterize the scenes which most unsettle Bill and drive him into the New York underworld: Nick Nightingale’s description of his next job, Alice’s description of her “nightmare,” and Bill’s fantasy of his wife with the naval officer. Almost all of the reds occur in the costume store (although oddly juxtaposed with the final scene at the toy store), which serve as the gateway to voluptuous pinks and violets. It’s interesting to note that, at the bottom of the saturation spectrum, Bill continues to go about his normal life—scenes from the office, from the hospital, walking home, etc.—completely oblivious of the turmoil above.

I’m excited to see how the rest of the visualizations come out. Obviously there is still tweaking to be done—I’m not sure what to make of the three or four vertical lines—and I’d like to be able to represent individual scenes in some way. Progress is slow, as each visualization takes close to an hour to complete. Nevertheless, we’ll soldier on; watch this space for further updates!

Conceiving of Kubrick, Quantifying Color

Because it was often not practical to collect data about the whole population, the idea of sampling was the foundation of 20th century applications of statistics.

In some application [sic] of media visualization, we face the same limitations. For instance, in our visualizations of Kingdom Hearts we sampled the complete videos of game play using a systematic sampling method. Ideally, if imageJ software was capable of creating a montage using all of video frames, we would not have to do this.

In preparation for this post, I attempted to use the imageJ software to create a montage using all of a video’s frames—specifically, those of the 1968 film 2001: A Space Odyssey. I used a program called Avidemux to extract each frame of the film and save it as a jpg, resulting in a total of 209,457 files (10.5 GB). Unfortunately, imageJ kept running out of memory before completing the montage creation process (what else would you expect from a program written in Java?). I’ve tweaked the settings a bit so that the montage only contains 1 out of every 100 frames at a fraction of their original size.  You can view the resulting image below:

Montage2

I chose 2001 for several reasons. Manovich’s work seems to focus almost exclusively on Vertoz’s films, especially on the length of their shots. Vertoz is of course well known for his rapid-fire juxtaposition of short shots, a trait shared with several other early Soviet directors. I was curious to see how Manovich’s visualization methods would look when applied to a film generally known for its longer shots. I also wanted to see a montage of a color film, especially one such as 2001 which uses color in such interesting ways.

Several patterns emerge, but I’m not sure how interesting they are. The film begins and ends (“The Dawn of Man” and “Jupiter and Beyond the Infinite”) with a pure black screen accompanied by György Ligeti’s soundtrack (there is also an intermission which follows this same formate. Colorful scenes similarly bookend the film, with drab blues and grays characterizing the two middle acts (“TMA-1” and “Jupiter Mission”).  Individual scenes seem to be relatively monochromatic—orange for primordial earth, white on the space station, red in HAL’s processor core, and blue in the mysterious apartment.

Undoubtedly, I’d have to spend a lot more time tweaking the visualizations in order to really stumble upon anything interesting, but the possibilities intrigue me. Joe’s post argues that the most compelling element of Manovich’s project is the visualizations of entire shots “averaged” into a single image. This allows us to analyze the degree of camera movement within individual shots, along with the movement vectors of objects being filmed. I feel like analysis of color could be another incredibly productive use of Manovich’s techniques. In part 7 of his “Visualizing Vertov” project, Manovich graphs Man with a Movie Camera‘s shots according to their grey scale x number of shapes. What if we graphed all the shots in 2001 according to their averaged hue x saturation? Over the course of random Googling in preparation for this source, I came across this site which features a “Movie Palette” of the film’s most commonly used colors. Although I’m not sure how the author arrived at this data, it seems like something which could be relevant to a Manovichian project.

To summarize in a single sentence (and provide another possible subtitle for our class): this stuff seems incredibly useful, but I have no idea how to use it. I’m still suspicious of quantitative analysis of words because their meaning is so thoroughly subjective, but things like color, camera movement, length of shots, and shapes feel a lot more like objective quantifiable data. I think further inquiry into this sub-field of DH has the potential to yield some really interesting and meaningful results.