[Edit: Removed redundant commentary about headline.]
In: Mathematics
So, first a bit of math. 138 minutes of 1080p HD video at 1.2 gigabytes would be around 1 megabit per second of data (note bits vs bytes). That’s quite low. I would not want to watch, say, a hockey or football game at that bit rate.
Which brings us to the secret sauce: motion compensation. If you look at each individual frame of the video and imagine it as an image on a web site, yes the video would be absurdly large or have horrible image quality at these file sizes. However what you must realize is that a video is many many images in a row. In fact if you took 2 consecutive frames from a video, they will be very similar to each other. Most video playback is done by retaining the last few frames of the video, and the data received makes use of that information. Having a working image to start with and then modifying it for the next frame consumes *far* less file space than starting from a blank canvas and drawing a new picture from scratch.
Which is why I bring up sports games. More motion over more of the image means more data is needed for the video to describe the changes, and sports tends to involve a lot of stuff moving and big camera sweeps and it takes data to describe all those image updates. On the other hand a video of a person sitting there looking at a camera (eg: the news) has much less motion and so fewer bits are needed to describe the changes to the picture.
Of course, if you don’t mind a poor quality image, you can just blur the heck out of the image. This makes the image description much simpler since it’s just colour blobs rather than intricate details. When the bit rate is set too low for encoding this is what can happen. Extreme amounts of motion – eg confetti dropping from the sky where each individual piece tumbles a bit differently – can ruin the image quality as any limit on how big the file can be is a problem when there’s *so much stuff moving independently at once* that it can’t be processed reasonably.
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