Search results
Results from the WOW.Com Content Network
Genetics compression algorithms are the latest generation of lossless algorithms that compress data (typically sequences of nucleotides) using both conventional compression algorithms and genetic algorithms adapted to the specific datatype. In 2012, a team of scientists from Johns Hopkins University published a genetic compression algorithm ...
Third-party benchmarking confirms that LZFSE decompresses faster than zlib, but also suggests that many other modern compression algorithms may have more favorable compression algorithm performance characteristics such as density, compression speed and decompression speed by a significant margin. [5]
Algorithms are generally quite specifically tuned to a particular type of file: for example, lossless audio compression programs do not work well on text files, and vice versa. In particular, files of random data cannot be consistently compressed by any conceivable lossless data compression algorithm; indeed, this result is used to define the ...
The LZ4 algorithm aims to provide a good trade-off between speed and compression ratio. Typically, it has a smaller (i.e., worse) compression ratio than the similar LZO algorithm, which in turn is worse than algorithms like DEFLATE. However, LZ4 compression speed is similar to LZO and several times faster than DEFLATE, while decompression speed ...
The name "entropy compression" was given to this method in a blog posting by Terence Tao [4] and has since been used for it by other researchers. [5] [6] [7]Moser's original version of the algorithmic Lovász local lemma, using this method, achieved weaker bounds than the original Lovász local lemma, which was originally formulated as an existence theorem without a constructive method for ...
This category deals with algorithms for data compression. Subcategories. This category has the following 3 subcategories, out of 3 total. D.
In information theory, an entropy coding (or entropy encoding) is any lossless data compression method that attempts to approach the lower bound declared by Shannon's source coding theorem, which states that any lossless data compression method must have an expected code length greater than or equal to the entropy of the source. [1]
Allows the user to adjust the balance between compression ratio and compression speed, without affecting the speed of decompression; LZO supports overlapping compression and in-place decompression. As a block compression algorithm, it compresses and decompresses blocks of data. Block size must be the same for compression and decompression.