Edit: Buy the movie poster hues (1914-2012) poster
A couple of weeks ago, I was having brunch with Kim-Mai Cutler — we were discussing the new startup I’m building in the enterprise space (if you’re a ui/ux person or awesome engineer looking for something fun to do, drop me a line!) — and I mentioned how I felt that most movie posters these days were very blue and dark. She didn’t fully believe me and challenged me to prove it. I looked around, and found some people had done this with a few posters over the last few years, but I became curious about the longer-term trends and what they would show. So, as any engineer would do, I wrote some code! (The code is open source and lives on github: image analysis.)
Edit: this post is up on Flowing Data, an awesome data visualization blog, YC Hacker news!, and Gizmodo. I will be doing a follow-on post with much better analysis and much more data. Follow @vijayp on twitter and stay tuned!
Visualizations:
The number of posters I was able to get varied based on the year:
I first made a unified view of colour trends in movie posters since 1914. Ignoring black and white colours, I generated a horizontal strip of hues in HSL. The width of each hue represents the amount of that hue across all images for that year, and the saturation and lighting were the weighted average for all matching pixels. Since HSL has a fixed order, comparisons can be made between years visually. (You can buy the movie poster hues poster here.) Click on the image below for a more detailed view:
Next, I made a similar unified view of generic colour trends in movie posters since 1914, but here lightness and saturation are both ignored. This makes the distribution of hues much more clear, but hides the average “darkness” of the photos.
Finally, I have created a pie chart representing the colour distribution of a specific year’s movie posters. (This should probably be animated and a line graph, more on that in the future work section)
Rationale:
First off, it is true that movie posters are much more blue, and much less orange than they used to be. QED 🙂 This page also talks about the blue/orange colours in movies.
This does appears to be a steady trend since 1915. Could this be related to evolution in the physical process of poster printing; what’s the effect of the economics and difficulty of producing posters over time? I also wonder whether moviemakers have become better at figuring out the “optimal” colour distribution of posters over time, and whether we’re asymptotically approaching some quiescent distribution.
I was a bit concerned that some of this might be due to bias in the data: some movies would be over-represented in the intra-year average (remember that some movies have multiple posters and I normalize over posters, not movies). I think this is not actually a huge issue because it’s reasonable to assume that a movie’s marketing budget is roughly proportional to the number of posters that it has produced for itself. This means that the skew, if any, would be similar to the perceived average.
I presented these preliminary data to some friends of mine who are more steeped in the world of graphics and arts. Cheryle Cranbourne, (she used to be a graphics designer and has just finished a Masters in interior architecture at RISD) had a number of good thoughts:
[Edit: I had misquoted this earlier] The movies whose posters I analysed “cover a good range of genres. Perhaps the colors say less about how movie posters’ colors as a whole and color trends, than they do about how genres of movies have evolved. For example, there are more action/thriller/sci-fi [films] than there were 50-70 years ago, which might have something to do with the increase in darker, more ‘masculine’ shades.”
This is backed up a bit by data from under consideration’s look at movie posters. They didn’t go back very far, but there did seem to be a reasonable correlation between movie age rating and palette.
She also pointed out that earlier posters were all illustrated/ hand painted, with fewer colors and less variation in tone. Perhaps the fact that white and black have become more prevalent is due to the change from illustration to photography. Painted skin might also over-represent orange and under-represent other hues that happen in real life.
Methodology:
I downloaded ~ 35k thumbnailed-size images (yay wget — “The Social Network” inspired me to not use curl) from a site that has a lot of movie posters online. I then grouped the movie posters by the year in which the movie they promoted was released. For each year, I counted the total number of pixels for each colour in the year. After normalizing and converting to HSL coordinates, I generated the above visualizations.
Inspirations:
I was inspired by Tyler Neylon’s great work on colour visualizations. I ended up writing my own code to do these image analysis visualizations, but I will try to integrate it with his work.
Future work:
There’s a bunch of stuff I still have to / want to do, but since I’m working on my startup, I don’t really have much time to focus on it right now. Here’s a long list of stuff:
- Follow up on all the open questions about the reasons for this change.
- Use other metadata (not just year) for movies to search for patterns. A simple machine learning algorithm should suffice if I throw all the attributes in at once. This should be able to highlight whether genre is important, and what other factors are crucial
- “main colour” analysis. I should run some kind of clustering (as Tyler does in his code). His code uses a handwritten (?) k-means clustering algorithm, which is a bit slow when faced with thousands of pictures worth of data. There are some faster albeit slightly less accurate versions that I could use.
- I need to move the pie charts to use gcharts js api, so they’re interactive
- I should probably make nicer/fancier js onhover stuff
- I should look at Bollywood and other sources to see whether this holds across countries.
- My visualizations and javascript aren’t so good. I have to learn how to do this stuff better!
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