#Computers #Compress #Text #Huffman #Coding #Huffman #Trees

When you put English text into a computer, it is saving every individual character as eight bits of data. Eight ones and zeros. I mean, they’re not actually ones and zeros, they’re particular positions of atoms on a disk or a few electrons going over a wire,

But as far as we’re concerned in the computer science world, they are ones and zeros. Bits. And yes, a modern phone or computer might store a few quadrillion of them(!) But every time you’ve had to wait a long time for a download, or your phone’s complained that its storage is full,

You’re running up against the same question that computer scientists have been working on since before these things here were state of the art: can we please not use so many bits? So, let’s run through how computers compress text. Images and video are different — that’s lossy compression,

Where it doesn’t matter if you lose a little bit of detail. But text has to be losslessly compressed: you can’t just lose a bit of detail, otherwise you end up sending the wrong worms. So the first thing we need to know is how text is stored on disk before it’s compressed.

To make this easier, I am just going to talk about English text here: I know only too well that it gets more complicated than this, but hey, the series is called “the basics”, so deal with it. On a modern computer, each English character takes up

Exactly eight ones and zeros on disk – eight bits, or one byte. And there are 256 possible combinations of those ones and zeros, so you can have 256 possible characters. That’s enough for the English alphabet, numbers, some punctuation, and… well, then it gets complicated depending on which country you’re in

And oh my god that’s a whole separate video we do not want to get into right now. Why eight bits? Because it was big enough to store all the characters that American computers usually needed, but no bigger, so the text doesn’t take up any more space than it absolutely has to.

Eight is also a power of two, which means it’s easy to deal with at the really low levels of programming. It’s also helpful to have a fixed number of bits per character because it makes searching through text really fast. If you want to go to the 100,000th character in some text,

You know exactly where the ones and zeros are going to be without having to count the number of bits in every single character before it. If you want to know how long a string of text is, you just count the bits and you divide by eight.

Computers don’t have the luxury of spaces, remember: it’s just one long string of 1s and 0s. But let’s say we’re not worried about speed right now, we just want to fit as much text as possible into as small a number of 1s and 0s as possible. Compression.

A good plan would be to assign the most common characters to smaller arrangements of bits. So those researchers, way way back, could have said: well, the space bar is generally used most often. Just give that the code “0”. Then it’s the lowercase e. Give that the code “1”.

Then it’s lowercase t: give that two zeros… …and immediately, we’ve hit a problem. Because the computer running through that text later has no way of knowing whether “00” is a t, or the space bar twice. And if we keep on assigning letters like that, the problems keep getting worse:

Is three zeros a lowercase n? Or a t followed by a space? Or three spaces? Remember, there are no gaps here, no actual space separators, all the computer can see is a constant stream of 1s and 0s. There’s no way to know which is meant.

Except. In 1952, a very clever mathematician called David Huffman invented Huffman coding. You invent something like that, your name gets put on it. Yes, there are much better, more modern, more complicated, mathematical ways to do this, but Huffman coding is the basic foundation of modern text compression. And here is how it works.

Let’s say we want to compress… let’s go with the lyrics to Will Smith’s “Wild Wild West”. Uncompressed, that is 3,684 characters taking up nearly 30,000 bits. First up: you count how many times each character is used, and you put that in a list in order.

Now that’ll be different for each block of text you’re compressing: this has way more Ws than usual. Now take the two least used characters. Those two are going to be the bottom branches on your “Huffman tree”. Write them down, with how often they’re used next to them. That’s called their frequency.

Then connect them together, one level up, with the sum of their frequencies. Now add that new sum back into your list, wherever it sits, higher up. And repeat! Take the bottom two off your list, connect them up, add the result back in your list. Keep that going.

And when one of the sums reaches the bottom two of the list, you connect it up like that. And eventually, you’re going to be down to one thing in your list, right at the top. You now have a Huffman tree, and it looks like this,

And it tells you how to convert your text into ones and zeros. Our first letter is the first uppercase W in ‘wiki-wiki-wild-wild-West’. Uppercase W is here, there’s 141 of them. So go the top, follow the path down. Each time you take the left hand side, write a 0;

Each time you take the right hand side, write a 1. So W is this: only 5 bits instead of 8. Next, lowercase i. Follow the code down, only four bits this time, it’s more common. Then the k, which is less common; that actually takes up seven bits. And you keep going.

Some of the letters will take up more than 8 bits, but that’s fine, because they’re not used very often. Now, you do have to also store this tree to provide a translation table between your new symbols and the uncompressed ones – so this is not efficient for short bits of text.

If you’re ever asked to do this in a computer science exam, then they’ll ignore all that and just ask you to do one easy word from a tree by hand. But for Will Smith’s magnum opus, we have compressed it down to just over 20,000 bits: about a 30% saving.

To uncompress the resulting stream of bits, it works the other way: just read across, take the left fork every time you see a 0 and the right fork every time you see a 1. When you reach a letter, that’s it, you know that’s the end,

And you know that there is no other path you could have possibly taken. You start again with the next bit. Now in practice, computers might do this working backwards from the bottom up sometimes, but this is a pretty good way to at least understand what’s going on. And here’s the really clever part:

Huffman proved that this is the most efficient way to assign 0s and 1s to single characters. It is mathematically impossible to beat this. Unless, as you might already have figured out, you start working on blocks bigger than one character. Clever tricks like that are basically how zip files work…

But then, this is just the basics. Thank you to all my proofreaders who helped with that script, thank you to my graphics team, and also thank you to the Centre for Computing History in Cambridge for letting me film with their old kit.

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