you can find in many sources this statement regarding LZ77 and LZ78.
They are both theoretically dictionary coders. LZ77 maintains a
sliding window during compression. This was equivalent to the
explicit dictionary constructed by LZ78 however, they are only
equivalent when the entire data is intended to be decompressed.
I have difficulties understanding this, in the text mentioned, equivalency of an LZ77 to LZ78 (explicit dictionary).
Could anybody explain it?
Related
I want to compress .txt files that contains dates in yyyy-mm-dd hh:mm:ss format and english words that sometimes tend to be repeated in different lines.
I read some articles about compression algorithm and find out that in my case dictionary based encoding is better than entropy based encoding. Since I want to implement algorithm myself I need something that isn't very complicated. So I paid attention to LZW and LZ77, but can't choose between them, because conclusions of articles I found are contradictory. According to some articles LZW has better compression ratio and according to others leader is LZ77. So the question is which one is most likely will be better in my case? Is there more easy-to-implement algorithms that can be good for my purpose?
LZW is obsolete. Modern, and even pretty old, LZ77 compressors outperform LZW.
In any case, you are the only one who can answer your question, since only you have examples of the data you want to compress. Simply experiment with various compression methods (zstd, xz, lz4, etc.) on your data and see what combination of compression ratio and speed meets your needs.
Is there any open source library or algorithm available to look at what phrases or words are most common among individual lines of text in a file and create a global dictionary that would then be used to compress the lines of text separately? Preferably the code if available would be in C or C++.
I found this question that I think was similar but did not have an answer that meets what I am looking for:
compressing a huge set of similar strings
There are three important things to recognize here.
The value of replacing a word by a code depends on its frequency and its length. Replacing "a" isn't worth a lot, even if it appears very often.
Once you've identified the most common words, phrases can be found by looking for occurrences of two common words appearing side by side. (In most grammars, word repetition is fairly rare.)
However, one of the biggest sources of redundancy in text is actually the amount of bits needed to predict the next letter. That's typically about 2, given the preceding text. Do you really need word-based compression when letter-based compression is so much easier?
I did some more searching, and I think I have found my answer.
I came across this page discussing improving compression by using boosters
http://mainroach.blogspot.com/2013/08/boosting-text-compression-with-dense.html
That page provided a link to the research paper
http://www.dcc.uchile.cl/~gnavarro/ps/tcj11.pdf
and also to the source code used to do the compression
http://vios.dc.fi.udc.es/codes/download.html
Yes. zlib, an open source compression library in C, provides the deflateSetDictonary() and inflateSetDictionary() routines for this purpose. You provide up to 32K of seed data in which the compressor will look for matching strings. The same dictionary needs to reside on both ends. If you are compressing lots of small pieces of data with a lot of commonality, this can greatly improve compression. Your "lines of text" certainly qualify as small pieces of data.
I have an opportunity to preset dictionary for deflate compression. It makes sense in my case, because data to be compressed is relatively small 1kb-3kb and I have a large sample of representative examples. Data to be compressed consists of arbitrary sequence of bytes, so tokenization etc. is not a good way to go. Also, data shows a lot of repetition (between data examples), so good dictionary could potentially give very good results.
The question is how calculate good dictionary? Is there an algorithm which calculates optimal dictionary (given sample data)?
I started looking at prefix trees, but it is not clear how to use them in this context.
Best regards,
Jarek
I am not aware of an algorithm to generate an optimal or even a good dictionary. This is generally done by hand. I think that a suffix tree would be a good approach to finding common strings for a dictionary, but I have never tried it.
The first thing to try is to simply concatenate 32K worth of your 1-3K examples and see how much gain that provides over no dictionary. Then you mess with it from there, changing the ordering of examples or pulling out repeated pieces in the examples to the end of the dictionary.
Note that the most common strings should be put at the end, since shorter distances take fewer bits.
I don't know how good this is, but it's a dictionary creator: https://github.com/vkrasnov/dictator
I have 3 main questions:
Let's say I have a large text file. (1)Is replacing the words with their rank an effective way to compress the file? (Got answer to this question. This is a bad idea.)
Also, I have come up with a new compression algorithm. I read some existing compression models that are used widely and I found out they use some pretty advanced concepts like statistical redundancy and probabilistic prediction. My algorithm does not use all these concepts and is a rather simple set of rules that need to be followed while compressing and decompressing. (2)My question is am I wasting my time trying to come up with a new compression algorithm without having enough knowledge about existing compression schemes?
(3)Furthermore, if I manage to successfully compress a string can I extend my algorithm to other content like videos, images etc.?
(I understand that the third question is difficult to answer without knowledge about the compression algorithm. But I am afraid the algorithm is so rudimentary and nascent I feel ashamed about sharing it. Please feel free to ignore the third question if you have to)
Your question doesn't make sense as it stands (see answer #2), but I'll try to rephrase and you can let me know if I capture your question. Would modeling text using the probability of individual words make for a good text compression algorithm? Answer: No. That would be a zeroth order model, and would not be able to take advantage of higher order correlations, such as the conditional probability of a given word following the previous word. Simple existing text compressors that look for matching strings and varied character probabilities would perform better.
Yes, you are wasting your time trying to come up with a new compression algorithm without having enough knowledge about existing compression schemes. You should first learn about the techniques that have been applied over time to model data, textual and others, and the approaches to use the modeled information to compress the data. You need to study what has already been researched for decades before developing a new approach.
The compression part may extend, but the modeling part won't.
Do you mean like having a ranking table of words sorted by frequency and assign smaller "symbols" to those words that are repeated the most, therefore reducing the amount of information that needs to be transmitted?
That's basically how Huffman Coding works, the problem with compression is that you always hit a limit somewhere along the road, of course, if the set of things that you try to compress follows a particular pattern/distribution then it's possible to be really efficient about it, but for general purposes (audio/video/text/encrypted data that appears to be random) there is no (and I believe that there can't be) "best" compression technique.
Huffman Coding uses frequency on letters. You can do the same with words or with letter frequency in more dimensions, i.e. combinations of letters and their frequency.
I am looking for a compression algorithm (for a programming competition) and I need a full description of how to implement it (all technical details), any loseless and patent-free algorithm will do, but the ease of implementation is a bonus :)
(Although possibly irrelevant) I plan to implement the algorithm in C++...
Thanks in advance.
EDIT:
I will be compressing text files only, no other file types...
Well, I can't go so far as to complete the competition for you, but please check out this article on wiki: Run Length Encoding. It is by far one of the simplest ways to compress data, albeit not always an efficient one. Compression is also domain specific, even amongst lossless algorithms you will find that what you are compressing determines how best to encode it.
RFC 1951 describes inflate/deflate, including a brief description of the compressor's algorithm. Antaeus Feldspar's An Explanation of the Deflate Algorithm provides a bit more background.
Also, the zlib source distribution contains a simplified reference inflater in contrib/puff/puff.c that can be helpful reading to understand exactly how the bits are arranged (but it doesn't contain a deflate, only inflate).
I'd start here on Wikipedia.
There's a whole lot to choose from, but without knowing more about what you want it's difficult to help more. Are you compressing text, images, video or just random files? Each one has it's own set of techniques and challenges for optimal results.
If ease of implementation is the sole criterion I'd use "filecopy" compression. Guaranteed compression ratio of exactly 1:1, and trivial implementation...
Huffman is good if you're compressing plain text. And all the commenters below assure me it's a joy to implement ;D
Ease of implementation: Huffman, as stated before. I believe LZW is no longer under patent, but I don't know for sure. It' a relatively simple algorithm. LZ77 should be available, though. Lastly, the Burrows-Wheeler transform allows for compression, but it's significantly more difficult to implement.
I like this introduction to the Burrows-Wheeler Transform.
If you go under "View" in your internet browser, there should be an option to either "Zoom Out" or make the text smaller.
Select one of those and...
BAM!
You just got more text on the same screen! Yay compression!
The Security Now! podcast recently put out an episode highlighting data compression algorithms. Steve Gibson gives a pretty good explanation of the basics of Huffman and Lempel-Ziv compression techniques. You can listen to the audio podcast or read the transcript for Episode 205.