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Email Address Sign up There was an error. Please try again. You're in! Thanks for signing up. There was an error. Tell us why! In traditional cameras the shutter opens and closes, exposing the light-sensitive medium for a fraction of a second. But the rain is falling nevertheless. To capture an image the camera system picks a point at which to start counting the raindrops, measuring the light that hits the sensor. Then it picks a point to stop.
Why not just always be recording? Theoretically you could, but it would drain the battery and produce a lot of heat. Fortunately, in the last few years image processing chips have gotten efficient enough that they can, when the camera app is open, keep a certain duration of that stream — limited resolution captures of the last 60 frames, for instance.
Context can mean a lot of things. It can be photographic elements like the lighting and distance to subject. But it can also be motion, objects, intention. A simple example of context is what is commonly referred to as HDR, or high dynamic range imagery. This technique uses multiple images taken in a row with different exposures to more accurately capture areas of the image that might have been underexposed or overexposed in a single exposure.
The context in this case is understanding which areas those are and how to intelligently combine the images together. This can be accomplished with exposure bracketing, a very old photographic technique, but it can be accomplished instantly and without warning if the image stream is being manipulated to produce multiple exposure ranges all the time. Context here is not simply the distance of a face, but an understanding of what parts of the image constitute a particular physical object, and the exact contours of that object.
This can be derived from motion in the stream, from stereo separation in multiple cameras, and from machine learning models that have been trained to identify and delineate human shapes. These techniques are only possible, first, because the requisite imagery has been captured from the stream in the first place an advance in image sensor and RAM speed , and second, because companies developed highly efficient algorithms to perform these calculations, trained on enormous data sets and immense amounts of computation time.
And this quality is entirely a function of the software engineering work and artistic oversight that goes into them. A system to tell good fake bokeh from bad. DxOMark did a comparison of some early artificial bokeh systems; the results, however, were somewhat unsatisfying.
It was less a question of which looked better, and more of whether they failed or succeeded in applying the effect. Computational photography is in such early days that it is enough for the feature to simply work to impress people.
Like a dog walking on its hind legs, we are amazed that it occurs at all. But Apple has pulled ahead with what some would say is an almost absurdly over-engineered solution to the bokeh problem.
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