S best binary compression algorithm


It is also often used as a component within lossy data compression technologies e. Lossless compression is used in cases where it is important that the original and the decompressed data be identical, or where deviations from the original data would be unfavourable. Typical examples are executable programs, text documents, and source code. Lossless audio formats are most often used for archiving or production purposes, while smaller lossy audio files are typically used on portable players and in other cases where storage space is limited or exact replication of the audio is unnecessary.

Most lossless compression programs do two things in sequence: Arithmetic coding achieves compression rates close to the best possible for a particular statistical model, which is given by the information entropy , whereas Huffman compression is simpler and faster but produces poor results for models that deal with symbol probabilities close to 1. There are two primary ways of constructing statistical models: This approach is simple and modular, but has the disadvantage that the model itself can be expensive to store, and also that it forces using a single model for all data being compressed, and so performs poorly on files that contain heterogeneous data.

Adaptive models dynamically update the model as the data is compressed. Both the encoder and decoder begin with a trivial model, yielding poor compression of initial data, but as they learn more about the data, performance improves.

Most popular types of compression used in practice now use adaptive coders. Lossless compression methods may be categorized according to the type of data they are designed to compress. While, in principle, any general-purpose lossless compression algorithm general-purpose meaning that they can accept any bitstring can be used on any type of data, many are unable to achieve significant compression on data that are not of the form for which they were designed to compress.

Many of the lossless compression techniques used for text also work reasonably well for indexed images. These techniques take advantage of the specific characteristics of images such as the common phenomenon of contiguous 2-D areas of similar tones. Every pixel but the first is replaced by the difference to its left neighbor. This leads to small values having a much higher probability than large values.

This is often also applied to sound files, and can compress files that contain mostly low frequencies and low volumes. For images, this step can be repeated by taking the difference to the top pixel, and then in videos, the difference to the pixel in the next frame can be taken.

A hierarchical version of this technique takes neighboring pairs of data points, stores their difference and sum, and on a higher level with lower resolution continues with the sums. This is called discrete wavelet transform. JPEG additionally uses data points from other pairs and multiplication factors to mix them into the difference. These factors must be integers, so that the result is an integer under all circumstances.

So the values are increased, increasing file size, but hopefully the distribution of values is more peaked. The adaptive encoding uses the probabilities from the previous sample in sound encoding, from the left and upper pixel in image encoding, and additionally from the previous frame in video encoding.

In the wavelet transformation, the probabilities are also passed through the hierarchy. Many of these methods are implemented in open-source and proprietary tools, particularly LZW and its variants.

Some algorithms are patented in the United States and other countries and their legal usage requires licensing by the patent holder. Because of patents on certain kinds of LZW compression, and in particular licensing practices by patent holder Unisys that many developers considered abusive, some open source proponents encouraged people to avoid using the Graphics Interchange Format GIF for compressing still image files in favor of Portable Network Graphics PNG , which combines the LZ77 -based deflate algorithm with a selection of domain-specific prediction filters.

However, the patents on LZW expired on June 20, Many of the lossless compression techniques used for text also work reasonably well for indexed images , but there are other techniques that do not work for typical text that are useful for some images particularly simple bitmaps , and other techniques that take advantage of the specific characteristics of images such as the common phenomenon of contiguous 2-D areas of similar tones, and the fact that color images usually have a preponderance of a limited range of colors out of those representable in the color space.

As mentioned previously, lossless sound compression is a somewhat specialized area. Lossless sound compression algorithms can take advantage of the repeating patterns shown by the wave-like nature of the data — essentially using autoregressive models to predict the "next" value and encoding the hopefully small difference between the expected value and the actual data.

It is sometimes beneficial to compress only the differences between two versions of a file or, in video compression , of successive images within a sequence. For example, while the process of compressing the error in the above-mentioned lossless audio compression scheme could be described as delta encoding from the approximated sound wave to the original sound wave, the approximated version of the sound wave is not meaningful in any other context.

By operation of the pigeonhole principle , no lossless compression algorithm can efficiently compress all possible data. For this reason, many different algorithms exist that are designed either with a specific type of input data in mind or with specific assumptions about what kinds of redundancy the uncompressed data are likely to contain.

See this list of lossless video codecs. Cryptosystems often compress data the "plaintext" before encryption for added security. When properly implemented, compression greatly increases the unicity distance by removing patterns that might facilitate cryptanalysis. However, many ordinary lossless compression algorithms produce headers, wrappers, tables, or other predictable output that might instead make cryptanalysis easier.

Thus, cryptosystems must utilize compression algorithms whose output does not contain these predictable patterns. Genetics compression algorithms not to be confused with genetic algorithms are the latest generation of lossless algorithms that compress data typically sequences of nucleotides using both conventional compression algorithms and specific algorithms adapted to genetic data.

In , a team of scientists from Johns Hopkins University published the first genetic compression algorithm that does not rely on external genetic databases for compression. Genomic sequence compression algorithms, also known as DNA sequence compressors, explore the fact that DNA sequences have characteristic properties, such as inverted repeats. The most successful compressors are XM and GeCo. Self-extracting executables contain a compressed application and a decompressor.

When executed, the decompressor transparently decompresses and runs the original application. This is especially often used in demo coding, where competitions are held for demos with strict size limits, as small as 1k. This type of compression is not strictly limited to binary executables, but can also be applied to scripts, such as JavaScript. Lossless compression algorithms and their implementations are routinely tested in head-to-head benchmarks. There are a number of better-known compression benchmarks.

Some benchmarks cover only the data compression ratio , so winners in these benchmarks may be unsuitable for everyday use due to the slow speed of the top performers. Another drawback of some benchmarks is that their data files are known, so some program writers may optimize their programs for best performance on a particular data set. The winners on these benchmarks often come from the class of context-mixing compression software.

The benchmarks listed in the 5th edition of the Handbook of Data Compression Springer, are: This approach is simple and modular, but has the disadvantage that the model itself can be expensive to store, and also that it forces using a single model for all data being compressed, and so performs poorly on files that contain heterogeneous data.

Adaptive models dynamically update the model as the data is compressed. Both the encoder and decoder begin with a trivial model, yielding poor compression of initial data, but as they learn more about the data, performance improves. Most popular types of compression used in practice now use adaptive coders. Lossless compression methods may be categorized according to the type of data they are designed to compress. While, in principle, any general-purpose lossless compression algorithm general-purpose meaning that they can accept any bitstring can be used on any type of data, many are unable to achieve significant compression on data that are not of the form for which they were designed to compress.

Many of the lossless compression techniques used for text also work reasonably well for indexed images. These techniques take advantage of the specific characteristics of images such as the common phenomenon of contiguous 2-D areas of similar tones.

Every pixel but the first is replaced by the difference to its left neighbor. This leads to small values having a much higher probability than large values. This is often also applied to sound files, and can compress files that contain mostly low frequencies and low volumes. For images, this step can be repeated by taking the difference to the top pixel, and then in videos, the difference to the pixel in the next frame can be taken. A hierarchical version of this technique takes neighboring pairs of data points, stores their difference and sum, and on a higher level with lower resolution continues with the sums.

This is called discrete wavelet transform. JPEG additionally uses data points from other pairs and multiplication factors to mix them into the difference. These factors must be integers, so that the result is an integer under all circumstances. So the values are increased, increasing file size, but hopefully the distribution of values is more peaked. The adaptive encoding uses the probabilities from the previous sample in sound encoding, from the left and upper pixel in image encoding, and additionally from the previous frame in video encoding.

In the wavelet transformation, the probabilities are also passed through the hierarchy. Many of these methods are implemented in open-source and proprietary tools, particularly LZW and its variants.

Some algorithms are patented in the United States and other countries and their legal usage requires licensing by the patent holder. Because of patents on certain kinds of LZW compression, and in particular licensing practices by patent holder Unisys that many developers considered abusive, some open source proponents encouraged people to avoid using the Graphics Interchange Format GIF for compressing still image files in favor of Portable Network Graphics PNG , which combines the LZ77 -based deflate algorithm with a selection of domain-specific prediction filters.

However, the patents on LZW expired on June 20, Many of the lossless compression techniques used for text also work reasonably well for indexed images , but there are other techniques that do not work for typical text that are useful for some images particularly simple bitmaps , and other techniques that take advantage of the specific characteristics of images such as the common phenomenon of contiguous 2-D areas of similar tones, and the fact that color images usually have a preponderance of a limited range of colors out of those representable in the color space.

As mentioned previously, lossless sound compression is a somewhat specialized area. Lossless sound compression algorithms can take advantage of the repeating patterns shown by the wave-like nature of the data — essentially using autoregressive models to predict the "next" value and encoding the hopefully small difference between the expected value and the actual data. It is sometimes beneficial to compress only the differences between two versions of a file or, in video compression , of successive images within a sequence.

For example, while the process of compressing the error in the above-mentioned lossless audio compression scheme could be described as delta encoding from the approximated sound wave to the original sound wave, the approximated version of the sound wave is not meaningful in any other context.

By operation of the pigeonhole principle , no lossless compression algorithm can efficiently compress all possible data. For this reason, many different algorithms exist that are designed either with a specific type of input data in mind or with specific assumptions about what kinds of redundancy the uncompressed data are likely to contain. See this list of lossless video codecs. Cryptosystems often compress data the "plaintext" before encryption for added security.

When properly implemented, compression greatly increases the unicity distance by removing patterns that might facilitate cryptanalysis.

However, many ordinary lossless compression algorithms produce headers, wrappers, tables, or other predictable output that might instead make cryptanalysis easier. Thus, cryptosystems must utilize compression algorithms whose output does not contain these predictable patterns.

Genetics compression algorithms not to be confused with genetic algorithms are the latest generation of lossless algorithms that compress data typically sequences of nucleotides using both conventional compression algorithms and specific algorithms adapted to genetic data. In , a team of scientists from Johns Hopkins University published the first genetic compression algorithm that does not rely on external genetic databases for compression. Genomic sequence compression algorithms, also known as DNA sequence compressors, explore the fact that DNA sequences have characteristic properties, such as inverted repeats.

The most successful compressors are XM and GeCo. Self-extracting executables contain a compressed application and a decompressor. When executed, the decompressor transparently decompresses and runs the original application. This is especially often used in demo coding, where competitions are held for demos with strict size limits, as small as 1k. This type of compression is not strictly limited to binary executables, but can also be applied to scripts, such as JavaScript.

Lossless compression algorithms and their implementations are routinely tested in head-to-head benchmarks. There are a number of better-known compression benchmarks. Some benchmarks cover only the data compression ratio , so winners in these benchmarks may be unsuitable for everyday use due to the slow speed of the top performers.

Another drawback of some benchmarks is that their data files are known, so some program writers may optimize their programs for best performance on a particular data set. The winners on these benchmarks often come from the class of context-mixing compression software. The benchmarks listed in the 5th edition of the Handbook of Data Compression Springer, are: Matt Mahoney , in his February edition of the free booklet Data Compression Explained , additionally lists the following: The Compression Ratings website published a chart summary of the "frontier" in compression ratio and time.

It produces measurements and charts with which users can compare the compression speed, decompression speed and compression ratio of the different compression methods and to examine how the compression level, buffer size and flushing operations affect the results.

The Squash Compression Benchmark uses the Squash library to compare more than 25 compression libraries in many different configurations using numerous different datasets on several different machines, and provides a web interface to help explore the results.

There are currently over 50, results to compare. Lossless data compression algorithms cannot guarantee compression for all input data sets. In other words, for any lossless data compression algorithm, there will be an input data set that does not get smaller when processed by the algorithm, and for any lossless data compression algorithm that makes at least one file smaller, there will be at least one file that it makes larger.