## Monday, December 16, 2013

### Finite State Entropy - A new breed of entropy coder

In compression theory, the entropy encoding stage is typically the last stage of a compression algorithm, the one where the gains from the model are realized.

The purpose of the entropy stage is to reduce a set of flags/symbol to their optimal space given their probability. As a simple example, if a flag has 50% to be set, you want to encode it using 1 bit. If a symbol has 25% probability to have value X, you want to encode it using 2 bits, and so on.
The optimal size to encode a probability is proven, and known as the Shannon limit. You can't beat that limit, you can only get close to it.

A solution to this problem has been worked for decades, starting with Claude Shannon own work, which were efficient but not optimal. The optimal solution was ultimately found by one of Shannon's own pupils, David A. Huffman, almost by chance. His version became immensely popular, not least because he could prove, a few years later, that his construction method was optimal : there was no way to build a better distribution.

Or so it was thought.
There was still a problem with Huffman encoding (and all previous ones) : an hidden assumption is that a symbol must be encoded using an integer number of bits. To say it simply, you can't go lower than 1 bit.
It seems reasonable, but that's not even close to Shannon's limit. An event which has 90% probability to happen for example should be encoded using 0.15 bits. You can't do that using Huffman trees.

A solution to this problem was found almost 30 years later, by Jorma Rissanen, under the name of Arithmetic coder. Explaining how it works is outside of the scope of this blog post, since it's complex and would require a few chapters; I invite you to read the Wikipedia page if you want to learn more about it. For the purpose of this presentation, it's enough to say that Arithmetic encoding, and its little brother Range encoding,  solved the fractional bit issue of Huffman, and with only some minimal losses to complain about due to rounding, get closer to Shannon limit. So close in fact that entropy encoding is, since then, considered a "solved problem".

Which is terrible because it gives the feeling that nothing better can be invented.

Well, there is more to this story. Of course, there is still a little problem with arithmetic encoders : they require arithmetic operations, such as multiplications, and divisions, and strictly defined rounding errors.
This is serious requirement for CPU, especially in the 80's. Moreover, some lawyer-happy companies such as IBM grabbed this opportunity to flood the field with multiple dubious patents on minor implementation details, making clear that anyone trying to use the method would face expensive litigation. Considering this environment, the method was barely used for the next few decades, Huffman remaining the algorithm of choice for the entropy stage.

Even today, with most of the patent issues cleared, modern CPU will still take a hit due to the divisions. Optimized versions can sometimes get away with the division during the encoding stage, but not the decoding stage (with the exception of the Binary arithmetic coding, which is however limited to 0/1 symbols).
As a consequence, arithmetic encoders are quite slower than Huffman ones. For low-end or even "retro" CPU, it's simply out of range.

It's been a long time objective of mine to bring arithmetic-level compression performance to vintage (or retro) CPU. Consider it a challenge. I've tested several variants, for example a mix of Huffman and Binary Arithmetic, which was free of divisions, but alas still needed multiplications, and required more registers to operate, which was overkill for weak CPU.

So I've been reading with a keen eye the ANS theory, from Jarek Duda, which I felt was heading into the same direction. If you are able to fully understand his paper, you are better than me, because quite frankly, most of the wording used in his document is way out of my reach. (Note : Jarek pointed to an update version of his paper, which should be easier to understand). Fortunately, it nonetheless resonated, because I was working on something very similar, and Jarek's document provided the last elements required to make it work.

And here is the result today, the Finite State Entropy coder, which is proposed in a BSD-license package at Github.

In a nutshell, this coder provides the same level of performance as Arithmetic coder, but only requires additions, masks, and shifts.
The speed of this implementation is fairly good, and even on modern high-end CPU, it can prove a valuable replacement to standard Huffman implementations.
Compared to zlib's Huffman entropy coder, it manages to outperform its compression ratio while besting it on speed, especially decoding speed.

Benchmark platform : Core i5-3340M (2.7GHz), Window Seven 64-bits
Benchmarked file : win98-lz4-run
Algorithm Ratio Compression Decompression
FSE       2.688   290 MS/s     415 MS/s
zlib      2.660   200 MS/s     220 MS/s

Benchmarked file : proba70.bin
Algorithm Ratio Compression Decompression
FSE       6.316   300 MS/s     420 MS/s
zlib      5.575   250 MS/s     270 MS/s

Benchmarked file : proba90.bin
Algorithm Ratio Compression Decompression
FSE       15.21   300 MS/s     420 MS/s
zlib      7.175   250 MS/s     285 MS/s

As could be guessed, the higher the compression ratio, the more efficient FSE becomes compared to Huffman, since Huffman can't break the "1 bit per symbol" limit.
FSE speed is also very stable, under all probabilities.

I'm quite please with the result, especially considering that, since the invention of arithmetic coding in the 70's, little new has been brought to this field.

-----------------------------------------------------

A little bit of History :

Jarek Duda's ANS theory was first published in 2007, and the paper received many revisions since then. Back in 2007, only Matt Mahoney had enough skill and willpower to sift through the complex theory, and morph it into a working code. However, Matt concentrated on the only use case of interest to him, the Binary version, called ABS, limited to 0/1 alphabet. This decision put his implementation in direct competition with the Binary Arithmetic Coder, which is very fast, efficient, and flexible. Basically, a losing ground for ANS. As a consequence, ANS theory looked uncompetitive, and slumbered during the next few years.

FSE work re-instates ANS as a competitive algorithm for multi-symbol alphabet (>2), concentrating its perspective as a viable alternative to block-based Huffman.

Thanks to promising early results from FSE, Jarek concentrated back its attention on multi-symbol alphabet. As we were chatting about perspectives and limitations of ANS, I underlined the requirement of a decoding table as a memory cost, and suggested a solution in the making to limit that issue (which ultimately failed). But Jarek took the challenge, and successfully created a new variant. He then published an updated version of his paper. The new method would be called rANS. He would later invent the terms tANS and rANS to distinguish the different methods.

rANS was later adapted by Fabian Giesen and Charles Bloom, producing some very promising implementations, notably vector-oriented code by Fabian.

But as said, this is not the direction selected for FSE, created before Jarek's paper revision. FSE is a finite state machine, created precisely to avoid any kind of multiplication, with an eye on low-power CPU requirements. It's interesting to note such radically different implementations can emerge from a common starting theory.

For the time being, FSE is still considered beta stuff, so please consider this release for testing purposes or private development environments.

Explaining how and why it works is pretty complex, and will require a few more posts, but bear with me, they will come in this blog.

Hopefully, with Jarek's document and these implementations now published, it will be harder this time for big corporations to confiscate an innovation from the public domain.

-----------------------------------------------------

List of Blog posts explaining FSE algorithm :

http://fastcompression.blogspot.com/2014/01/fse-decoding-how-it-works.html
http://fastcompression.blogspot.com/2014/01/huffman-comparison-with-fse.html
http://fastcompression.blogspot.com/2014/02/a-comparison-of-arithmetic-encoding.html
http://fastcompression.blogspot.com/2014/02/fse-defining-optimal-subranges.html
http://fastcompression.blogspot.com/2014/02/fse-distributing-symbol-values.html
http://fastcompression.blogspot.com/2014/02/fse-decoding-wrap-up.html
http://fastcompression.blogspot.com/2014/02/fse-encoding-how-it-works.html
http://fastcompression.blogspot.com/2014/02/fse-encoding-part-2.html
http://fastcompression.blogspot.com/2014/02/fse-tricks-memory-efficient-subrange.html