Hi, this is Wayne again with a topic “Data Compression as Fast As Possible”.
Anyone who’s ever had to pack for a trip has probably known the frustration of trying to cram almost a suitcase worth of stuff into a backpack. I mean sure we could suck it up and leave a few items behind or haul around our bulky luggage with us. If they’re so important, but if we did that, then our bag won’t fit in the overhead compartment anyway, and all we’re trying to bring is a few extra pairs of underwear. Just in case we eat some bad tandoori chicken. Well, that got gross but other than that was pretty much a perfect analogy for why data compression became so important to reduce storage requirements and to improve transfer speeds over standard connections.
These packaging services are kind of like our roommate who catches us standing on our bag. Trying to zip it up and says you know you can’t just roll your laundry and save like a ton of space right and as it turns out when we try it for ourselves. The smug little turd is right. Not only does all of our stuff now fit, but we even have more room to crammed with stuff will never use like that copy of ET the extra-terrestrial that we definitely don’t watch every night and cry back on topic. Obviously, data can’t be rolled in quite the same manner as slightly pre-warn laundry, so coding engineers devised algorithms capable of breaking down longer strings of data into shorter ones, then reassembling them later, using what they’ve retained an easy way to describe.
This would be like breaking down the equation: 2 times 2 times 2 times 2 times 2 into 2 to the fifth power, which is much faster to write and simpler to articulate. But I actually know a thing or two about coding, and one of those things is that the use of exponential symbols is simply not possible. Well, first off, thankfully, we weren’t roommates in college.
I mean talk about smug, second you’re correct as well. While it is possible to write a command string which describes an exponential function, it usually isn’t much of a space saver, so maybe a better way to demonstrate how a compression algorithm works is to use something like a string of X’s and O’s or zeros and ones. If you’re catching the similarities here, let’s say we have a string of eight digits, which is spelled out as X, X, X, 0 X, X X. Now an algorithm look at this and decide that every time it comes across a new digit, it will replicate it and then note how many times it’s been repeated.
This would cause our string to transform into the smaller X 3 o 2 X 3. While this did reduce overall size, it only managed to do so by 25 %, which isn’t exactly going to allow us to cram our drives full or transfer large files over the internet more efficiently. That’S where more specifically designed algorithms will come in to strip the code to its most basic form, instead simply adding all the X’s together by treating each as a value of 1, then listing all the O’s followed with a digit count as before. Then we end up with a string that would look like 302 3, which we can now see as half the size of the original code.
Now, obviously, this example is rudimentary at best, but it gives an idea of how mathematicians are able to achieve and then further improve upon this feat, as time goes on now. This example would be known as lossless compression. All of the original data remains intact and is a requirement for programs or documents, but if you’re willing to give up some detail in the original file, lossy compression algorithms can be used for media files like music and movies. As long as you understand that the tighter you cram it, the lower the playback quality you’ll experience, but you can actually learn a little bit more about here in our video specifically about video compression speaking of compression.
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