 Coder Spirit

# Exploring the LZ77 Algorithm

This article is the first in a series where we’ll delve into the fascinating world of compression algorithms, starting with LZ77 (a lossless data compression algorithm). In future articles, we’ll expand on its family: LZ78, LZW, LZSS, DEFLATE, and more.

You might be wondering, why explore a topic that’s already been covered extensively in numerous articles and books? Well, I have some reasons:

• During my previous work on (animated gosper curves), I found myself in need of reliable TypeScript/JavaScript libraries that could handle GIT, PNG and APNG files. However, the available options were either poorly maintained, tied to NodeJS, or not compatible with ECMAScript modules (ESM). This prompted me to create my own solution and document the journey.
• I noticed a lack of engaging, visual and interactive explanations on the topic. This series aims to fill the gap by providing not just information, but also interactive simulations to help you understand these algorithms.

Stay tuned as we embark on this exciting journey of data compression!

## A bit of history

LZ77 was first published in 1977 by Abraham Lempel and Jacob Ziv. It was by no means the first compression algorithm, but it was the first to introduce a sliding window that allowed processing arbitrary streams of data in a nearly optimal way without needing prior knowledge of the data.

## Some terms & concepts

Before we dive into the algorithm, let’s define some terms and concepts that will come in handy later on (don’t worry if you don’t understand them yet, they are here so you can refer to them later on):

• The input stream is the sequence of symbols that we want to compress.
• The output stream is the sequence of symbols that we get after compressing the input stream.
• A character is a single symbol in the input stream, our basic data element.
• The sliding window is a fixed-size¹ buffer that acts as a view into the input stream. It’s composed of two parts:
• The search buffer is the part of the sliding window that contains the symbols that have already been processed.²
• The look-ahead buffer is the part of the sliding window that contains the symbols that are yet to be processed.²
• The coding position is the position of the first symbol in the look-ahead buffer (but we use its absolute index respect to the input stream to refer to it).
• A pointer is a pair of numbers that points to a sequence of symbols in the search buffer. In this article, we’ll use the notation <length:offset>, but other notations are also used elsewhere. Also, the offset is often expressed respect to the coding position, but we’ll compute it respect to the start of the search buffer.
1. The size of the sliding window is configurable, but it’s usually a power of 2. Also, although its size is fixed during the process, it can be treated as if it was smaller at the beginning and end of the compression process.

2. As with the sliding window, the sizes of the search and look-ahead buffers are also configurable.

## The algorithm

The algorithm is quite simple, it can be summarized in the following steps (don’t fret, we’ll see a simulation of it later on):

1. Initialize the sliding window with the first symbols of the input stream, and set the coding position to the beginning of the stream.
2. Find the longest sequence of symbols in the search buffer that matches the symbols in the look-ahead buffer (notice that we can find matches that span across both buffers!).
3. Output a <length:offset:char> tuple (a pointer followed by the first symbol in the look-ahead buffer that doesn’t match the sequence) to the output stream.
4. If the look-ahead buffer is empty, we are done. Otherwise, move the sliding window as many symbols “to the right” as the length of the previous match, and repeat from step 2.

Notice that the algorithm does not prescribe the format of the output tuples, nor the size of the buffers, nor the pattern matching algorithm.

## Compression examples

Let’s see a simulation of the algorithm in action! For these examples we’ll use a search buffer of size 4 and a look-ahead buffer of size 4.

You can press the play button to advance the simulation step by step.

The previous example is helpful, but it does not show how the match can span across both buffers. Let’s see it in action:

By the way, did you notice that the output stream for the second example is shorter than the first one? That’s because the second example has more redundancy (more repeated patterns), so it can be compressed more efficiently.

## A decompression example

Now that we have seen how the compression process works, let’s see how we can do it in reverse to recover the original input stream. For this example we’ll use the output stream from the previous one.

When decompressing, we reconstruct the search buffer on the fly. In our case we do it character by character, because some of the pointers point to symbols that are yet to be decompressed.

Besides the minor complication of having pointers to symbols that are yet to be decompressed, the decompression process is simpler and faster than compression (we don’t have to search for longest matches between strings).

## Some observations

Up until this point, we have paid attention to the algorithm, however, apart from the algorithm itself, there are at least 3 other factors that affect how well it performs:

• The pattern matching algorithm (how we find the longest match) affects both the speed and memory usage.
• The output format (how we encode the pointers) affects the compression ratio.
• The parametrization (how big the search and look-ahead buffers are) directly affects the compression ratio and memory usage, but it can also affect the compression ratio indirectly by affecting the output format (a larger sliding window results in larger pointers, but it also increases the likelihood of finding longer matches).

Depending on how “clever” is the implementation, other relations between these factors and performance attributes can arise.

For example, let’s imagine an encoding that uses smaller pointers for the first bytes of the stream (this is possible because the search buffer is smaller at the beginning of the compression process). This would result in a slightly better compression ratio, but the added complexity could affect how fast the algorithm is.

## The pattern matching algorithm

The naive approach for pattern matching is easy to understand and implement, but has a quadratic time complexity of $O(m·n)$ (where $m$ is the length of the pattern and $n$ is the length of the text).

A better (but not necessarily the best) approach is to use the Knuth-Morris-Pratt algorithm, which has a time complexity of $O(m+n)$.

For the interactive examples in this article I implemented a modified version of KMP to account for partial matches and the fact that although we accept matches that overlap over the search and look-ahead buffers, we don’t want matches with an offset greater than the size of the search buffer.

/**
* Custom version of Knuth-Morris-Pratt algorithm that also returns the longest
* partial match if no full match is found. It also introduces stopAfter
* parameter to stop searching after a certain point.
*/
export const partialKMPSearch = (
searchString: string,
pattern: string,
stopAfter: number = +Infinity
): [number, number] => {
const n = searchString.length;
const m = pattern.length;

const table = kmpPreprocessPattern(pattern);

let i = 0;
let j = 0;

let longestMatchLength = 0;
let longestMatchIndex = -1;

while (j < n) {
if (j - i >= stopAfter) {
break;
}
if (pattern[i] === searchString[j]) {
j += 1;
i += 1;
if (i === m) {
return [j - i, i];
}
} else {
if (i === 0) {
j += 1;
} else {
if (i > longestMatchLength) {
longestMatchLength = i;
longestMatchIndex = j - i;
}

i = table[i - 1];
}
}
}

return [longestMatchIndex, longestMatchLength];
};

/**
* Preprocesses the pattern to generage the skip table used by the KMP algorithm
*/
const kmpPreprocessPattern = (pattern: string): number[] => {
const m = pattern.length;
const table = new Array(m).fill(0);

let i = 0;
let j = 1;

while (j < m) {
if (pattern[i] === pattern[j]) {
i += 1;
table[j] = i;
j += 1;
} else if (i === 0) {
table[j] = 0;
j += 1;
} else {
i = table[i - 1];
}
}

return table;
};

Choosing the right pattern matching algorithm deserves an article of its own. In my case, I chose KMP because it’s easy to implement and easy to adapt for partial matches. However, it’s important to note that there are other notable algorithms that may be worth exploring, such as Boyer-Moore, Rabin-Karp, and many others (such as algorithms relying on suffix trees or suffix arrays).

## Parametrization & Encoding

There are many tradeoffs to consider when choosing an output format:

• How many bits do we use for the length and offset?
• Going further: Do we use a fixed or variable number of bits for the length and offset?
• Should we use “fat pointers” (pointers that include the next symbol), or “thin pointers” (pointers that only include the length and offset)?
• In the case of “fat pointers” we could skip encoding the offset when the length is 0.
• In the case of “thin pointers”, we could distinguish between pointers and symbols by looking at the first bit (0 for symbols, 1 for pointers).

## Conclusion

As we have seen, LZ77 is a simple yet powerful lossless compression algorithm, or rather, a family of algorithms: It leaves many design decisions to the implementer, which can result in a wide range of implementations with different performance characteristics.

We have only scratched the surface, but I hope this article has piqued your interest in the topic. In future articles we’ll explore other compression algorithms, as well as some of the topics we have left out in this one.