This has been described as “using visualization,” but I disagree. Radiohead’s recent video for their song House of Cards was “shot without cameras,” using LIDAR and structured light real-time 3D imaging devices. A poetry visualization I discussed earlier has the same properties. But they also fail the readability test, you cannot tell which image represents which filename. Since they are based on data and are visual, they could be a visualization. VisualIDs are a very clever idea to help the user tell files apart: they produce images from the names of files to produce visually similar (but still distinct) icons for files with similar names. But music visualization plugins are not visualizations in the pragmatic/information visualization sense. But what about readability? Can you tell which song was played when the image above was created? This is not a shortcoming, it’s simply not the goal of a music visualization to be readable (and it would be very difficult). Since it creates images from wave data, it clearly fulfills the first two criteria. Not only does it have a huge range of different styles that it can transition between, it’s also very good at detecting beats and different instruments, so the visualization really fits the music. MilkDrop is one of the most impressive music visualizers. The following examples show how these criteria provide a clear separation of visualization (in the sense of scientific and information visualization) and other kinds of data transformations that result in images. This definition was published in a paper on Visualization Criticism, part of which I discussed in an earlier posting. The visualization must also be recognizable as one and not pretend to be something else (see the discussion of Informative Art). Any transformation of non-trivial data into an image will leave out information, but there must be at least some relevant aspects of the data that can be read. The most important criteria is that the visualization must provide a way to learn something about the data.
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