Lossless compression is a class of data compression that allows the original data to be perfectly reconstructed from the compressed data with no loss of information. Lossless compression is possible because most real-world data exhibits statistical redundancy.[1] By contrast, lossy compression permits reconstruction only of an approximation of the original data, though usually with greatly improved compression rates (and therefore reduced media sizes).
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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. Therefore, compression ratios tend to be stronger on human- and machine-readable documents and code in comparison to entropic binary data (random bytes).[2]
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. Some image file formats, like PNG or GIF, use only lossless compression, while others like TIFF and MNG may use either lossless or lossy methods. 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.
There are two primary ways of constructing statistical models: in a static model, the data is analyzed and a model is constructed, then this model is stored with the compressed data. 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.
No lossless compression algorithm can efficiently compress all possible data (see the section Limitations below for details). 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.
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.
Most practical compression algorithms provide an "escape" facility that can turn off the normal coding for files that would become longer by being encoded. In theory, only a single additional bit is required to tell the decoder that the normal coding has been turned off for the entire input; however, most encoding algorithms use at least one full byte (and typically more than one) for this purpose. For example, deflate compressed files never need to grow by more than 5 bytes per 65,535 bytes of input.
In fact, if we consider files of length N, if all files were equally probable, then for any lossless compression that reduces the size of some file, the expected length of a compressed file (averaged over all possible files of length N) must necessarily be greater than N.[20] So if we know nothing about the properties of the data we are compressing, we might as well not compress it at all. A lossless compression algorithm is useful only when we are more likely to compress certain types of files than others; then the algorithm could be designed to compress those types of data better.
The "trick" that allows lossless compression algorithms, used on the type of data they were designed for, to consistently compress such files to a shorter form is that the files the algorithms are designed to act on all have some form of easily modeled redundancy that the algorithm is designed to remove, and thus belong to the subset of files that that algorithm can make shorter, whereas other files would not get compressed or even get bigger. 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 concept of randomness in Kolmogorov complexity.[21]
On the other hand, it has also been proven[22] that there is no algorithm to determine whether a file is incompressible in the sense of Kolmogorov complexity. Hence it is possible that any particular file, even if it appears random, may be significantly compressed, even including the size of the decompressor. An example is the digits of the mathematical constant pi, which appear random but can be generated by a very small program. However, even though it cannot be determined whether a particular file is incompressible, a simple theorem about incompressible strings shows that over 99% of files of any given length cannot be compressed by more than one byte (including the size of the decompressor).
Real compression algorithm designers accept that streams of high information entropy cannot be compressed, and accordingly, include facilities for detecting and handling this condition. An obvious way of detection is applying a raw compression algorithm and testing if its output is smaller than its input. Sometimes, detection is made by heuristics; for example, a compression application may consider files whose names end in ".zip", ".arj" or ".lha" uncompressible without any more sophisticated detection. A common way of handling this situation is quoting input, or uncompressible parts of the input in the output, minimizing the compression overhead. For example, the zip data format specifies the 'compression method' of 'Stored' for input files that have been copied into the archive verbatim.[24]
Following its announcement in might 2006, Grand Theft Auto IV highly compressed was widely anticipated. it had been discharged for the PlayStation three and Xbox 360 consoles in Apr 2008, and for Microsoft Windows in Dec 2008.
Laser-plasma interactions in the novel regime of relativistically-induced transparency have been harnessed to generate efficiently intense ion beams with average energies exceeding 10 MeV/nucleon (>100 MeV for protons) at ``table-top'' scales. We have discovered and utilized a self-organizing scheme that exploits persisting self-generated plasma electric ( 0.1 TV/m) and magnetic ( 104 Tesla) fields to reduce the ion-energy (Ei) spread after the laser exits the plasma, thus separating acceleration from spread reduction. In this way we routinely generate aluminum and carbon beams with narrow spectral peaks at Ei up to 310 MeV and 220 MeV, respectively, with high efficiency ( 5%). The experimental demonstration has been done at the LANL Trident laser with 0.12 PW, high-contrast, 0.65 ps Gaussian laser pulses irradiating planar foils up to 250 nm thick. In this regime, Ei scales empirically with laser intensity (I) as I 1 / 2. Our progress is enabled by high-fidelity, massive computer simulations of the experiments. This work advances next-generation compact accelerators suitable for new applications. E . g ., a carbon beam with Ei 400 MeV and 10% energy spread is suitable for fast ignition (FI) of compressed DT. The observed scaling suggests that is feasible with existing target fabrication and PW-laser technologies, using a sub-ps laser pulse with I 2.5 1021 W/cm2. These beams have been used on Trident to generate warm-dense matter at solid-densities, enabling us to investigate its equation of state and mixing of heterogeneous interfaces purely by plasma effects distinct from hydrodynamics. They also drive an intense neutron-beam source with great promise for important applications such as active interrogation of shielded nuclear materials. Considerations on controlling ion-beam divergence for their increased utility are discussed. Funded by the LANL LDRD program.
A scheme of a neutral beam injector (NBI), based on electrostatic acceleration and magneto-static deflection of negative ions, is proposed and analyzed in terms of feasibility and performance. The scheme is based on the deflection of a high energy (2 MeV) and high current (some tens of amperes) negative ion beam by a large magnetic deflector placed between the Beam Source (BS) and the neutralizer. This scheme has the potential of solving two key issues, which at present limit the applicability of a NBI to a fusion reactor: the maximum achievable acceleration voltage and the direct exposure of the BS to the flux of neutrons and radiation coming from the fusion reactor. In order to solve these two issues, a magnetic deflector is proposed to screen the BS from direct exposure to radiation and neutrons so that the voltage insulation between the electrostatic accelerator and the grounded vessel can be enhanced by using compressed SF6 instead of vacuum so that the negative ions can be accelerated at energies higher than 1 MeV. By solving the beam transport with different magnetic deflector properties, an optimum scheme has been found which is shown to be effective to guarantee both the steering effect and the beam aiming. 2ff7e9595c
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