The Lempel-Ziv (LZ) compression method is one of the most popular lossless storage algorithms.
Data Compression
DEFLATE is a variant of LZ that is optimized for decompression speed and compression ratio, although its compression speed can be very slow, PKZIP , gzip and PNG all use DEFLATE. LZW (Lempel-Ziv-Welch) was a Unisys patent until the patent expired in June 2003, this method was used for GIF images. Also worth mentioning is the LZR (LZ-Renau) method, which is the basis of the Zip method. The LZ method uses a table-based compression model, in which table entries are replaced with repeated data strings. For most LZ methods, this table is dynamically generated from the initial input data. This table is often maintained using Huffman coding (eg SHRI, LZX). A current LZ-based encoding scheme that works well is LZX , which is used in Microsoft’s CAB format.
The best compression tools use probabilistic model predictions for arithmetic coding. Arithmetic coding was invented by the Finnish information theorist Jorma Rissanen and turned into a practical method by Witten, Neal and Cleary. This approach allows better compression than the well-known Huffman algorithm and is well suited for adaptive data compression, where predictions are context sensitive. Arithmetic encoding has been used in the JBIG binary image compression standard, the DejaVu document compression standard. The Dasher text input system is a reverse arithmetic encoder.
A very simple compression method is run-length encoding, which replaces the same continuous data with simple data-length encoding, which is an example of lossless data compression.
Data Compression
This method is often used on office computers to make better use of disk space, or to make better use of bandwidth on a computer network. Losslessness is a very important requirement for symbolic data such as spreadsheets, text, executables, etc., because in most cases even a single bit of data change is unacceptable, except in some limited cases.
For video and audio data, some level of quality degradation is acceptable as long as a significant portion of the data is not lost. Taking advantage of the limitations of the human perception system, a lot of storage space can be saved and the quality of the results obtained does not differ significantly from the quality of the original data. These lossy data compression methods generally require a trade-off between compression speed, compressed data size, and quality loss.
Lossy image compression is used in digital cameras to dramatically increase storage capacity with little degradation in image quality. Video compression with lossy MPEG-2 codec for DVD implements a similar function.
In lossy audio compression, psychoacoustic methods are used to remove inaudible or hard-to-hear components of a signal. Human speech compression often uses more specialized techniques, so “speech compression” or “speech coding” is also sometimes distinguished from “audio compression” as a separate field of study. Different audio and speech compression standards fall under the category of audio codecs. For example, voice compression is used for Internet telephony, while audio compression is used for ripping and decoding CDs using MP3 players.
theory
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Compression theory (which is closely related to algorithmic information theory) and rate distortion theory, research work in this area was established primarily by the American academic Claude Elwood Shannon, who in the late 1990s In the 1940s and 1950s, fundamental articles were published on the subject. in the early 1900s. Doyle and Carlson wrote in 2000 that data compression “is one of the simplest and most elegant design theories in all engineering fields.” Cryptography and coding theory are also closely related disciplines, and the idea of data compression and statistical inference also have deep roots.
Many lossless data compression systems can be viewed as a four-step model, and lossy data compression systems generally contain more steps, such as prediction, frequency transformation, and quantization.
The process of encoding information using fewer bits than the original representation
Data compression
In computer science and information theory, data compression or source coding is the process of representing information with fewer data bits (or other information-related units) than if it were not encoded, according to an encoding mechanism specific . For example, if we encode “compression” as “comp”, the item can be represented with fewer data bits. A common example is the ZIP archive format, which not only provides compression but also acts as an archiver, capable of storing many files in the same archive.
We can use data consistency (represented by information entropy, entropy), regularity, and predictability to achieve data compression. The compression technology first developed by humans is actually natural language. Generally speaking, if a thing can be described in a relatively simplified natural language, then it will be better able to compress such things. The more consistent the data, the more concentrated its statistical features. Take image compression as an example, which centrally accounts for the time domain and frequency domain of the Fourier transform, the histogram, and the eigenvalues.
Data compression is possible because most real-world data has statistical redundancy. For example, the letter “e” is more commonly used in English than the letter “z”, and it is very unlikely that the letter “q” will be followed by a “z”. Non-destructive data compression generally exploits statistical redundancy so that the sender’s data can be represented more succinctly, but fully.
The compression ratio of non-destructive data compression is not sufficient to handle the large volume of audio and video data, but if some loss of fidelity is allowed, higher compression can be achieved. For example, when people look at photographs or television images, they may not realize that some details are not perfect. Similarly, two audio recording sample streams may sound the same, but they are not actually exactly the same. Destructive data compression uses fewer bits to represent images, video, or audio with acceptable or imperceptible numbers.
However, there are often files that cannot be compressed using destructive data compression, and in fact cannot be compressed using any compression algorithm for data that does not contain discernible patterns. Also, trying to compress already compressed data often results in data bloat.
In fact, destructive data compression will eventually get to the point where it won’t work. For example, an extreme example: the compression algorithm deletes the last byte of the file every time, and after this algorithm continues to compress until the file is empty, the compression algorithm will not continue to work.
Compression is important because it helps reduce the consumption of expensive resources such as hard drive space and connection bandwidth, however, compression requires information processing resources, which can also be expensive. Therefore, the design of the data compression mechanism requires a compromise between the compression capacity, the degree of distortion, the computing resources required, and various other factors that must be taken into account.
As with any form of communication, compressed data communication only works if both the sender and receiver of the information understand the encryption mechanism. For example, the article only makes sense if the recipient knows that the article is to be interpreted in Chinese characters. Also, the compressed data can only be understood by the receiver if he knows the encoding method.
In fact, destructive data compression will eventually get to the point where it won’t work. For example, an extreme example: the compression algorithm deletes the last byte of the file every time, and after this algorithm continues to compress until the file is empty, the compression algorithm will not continue to work.
Compression is important because it helps reduce the consumption of expensive resources such as hard drive space and connection bandwidth, however, compression requires information processing resources, which can also be expensive. Therefore, the design of the data compression mechanism must compromise on compression capability, degree of distortion, required computing resources, and various other factors that must be taken into account.
As with any form of communication, compressed data communication only works if both the sender and receiver of the information understand the encryption mechanism. For example, the article only makes sense if the recipient knows that the article is to be interpreted in Chinese characters. Also, the compressed data can only be understood by the receiver if he knows the encoding method.
In fact, destructive data compression will eventually get to the point where it won’t work. For example, an extreme example: the compression algorithm deletes the last byte of the file every time, and after this algorithm continues to compress until the file is empty, the compression algorithm will not continue to work.
Compression is important because it helps reduce the consumption of expensive resources such as hard drive space and connection bandwidth, however, compression requires information processing resources, which can also be expensive. Therefore, the design of the data compression mechanism must compromise on compression capability, degree of distortion, required computing resources, and various other factors that must be taken into account.
As with any form of communication, compressed data communication only works if both the sender and receiver of the information understand the encryption mechanism. For example, the article only makes sense if the recipient knows that the article is to be interpreted in Chinese characters. Also, compressed data can only be understood by the receiver if it knows the encoding method.
In computer science and information theory, data compression or source coding is the process of representing information with fewer data bits (or other information-related units) than if it were not encoded, according to an encoding mechanism specific .
For example, if we encode “compression” as “comp”, the item can be represented with fewer data bits. A common example is the ZIP archive format, which not only provides compression but also acts as an archiver, capable of storing many files in the same archive.
We can use data consistency (represented by information entropy, entropy), regularity, and predictability to achieve data compression. The compression technology first developed by humans is actually natural language. Generally speaking, if a thing can be described in a relatively simplified natural language, then it will be better able to compress such things.
The more consistent the data, the more concentrated its statistical features. Taking image compression as an example, it centrally represents the time domain and frequency domain of the Fourier transform, the histogram, and the eigenvalues.
Data compression is possible because most real-world data has statistical redundancy. For example, the letter “e” is more commonly used in English than the letter “z”, and it is very unlikely that the letter “q” will be followed by a “z”. Non-destructive data compression generally exploits statistical redundancy so that the sender’s data can be represented more succinctly, but fully.
The compression ratio of non-destructive data compression is not sufficient to handle large volumes of audio and video data, but higher compression can be achieved if some loss of fidelity is tolerated. For example, when people look at photographs or television images, they may not realize that some details are not perfect. Similarly, two audio recording sample streams may sound the same, but they are not actually exactly the same. Destructive data compression uses fewer bits to represent images, video, or audio with acceptable or imperceptible numbers.
However, there are often files that cannot be compressed using destructive data compression, and in fact cannot be compressed using any compression algorithm for data that does not contain discernible patterns. Also, trying to compress already compressed data often results in data bloat.
In the field of professional sound, a compressor is an electronic sound processor designed to reduce the dynamic range of the signal without noticing its presence too much. This task is done by reducing the system gain, when the signal exceeds a certain threshold.
Traditionally, compressors have been electronic equipment with one or two rack units, but software versions of them have appeared for some years.
A compressor acts in such a way that it attenuates the electrical signal by a certain amount (normally measured in decibels) and from a certain input level. The objective is to ensure that the resulting dynamic excursion is lower than the original, to protect certain equipment against possible signal peaks or, if it is a saturated sound, to try to hide the error.
Reasons to compress a signal
-Control the energy of the signal: The human ear is very sensitive, so the compression must be smooth and subtle so as not to capture it. This type of compression is used when there is a signal in which the intensity varies, so it is compressed to achieve a more constant signal within the values assigned to it.
-Control the peak level of the signal: Often the equipment is limited, so the amplifiers can saturate and therefore be damaged. In this case the compression is used to control the signal and thus protect the equipment.
-Reduce the dynamic range of the signal: By attenuating the peaks of a signal, we reduce its dynamic range. Many devices are limited by the peaks, and this allows the RMS level of the signal to be raised.
Compressor Uses
In the field of music, its use ranges from applications for musical recordings to live sound. For example, it is often used to add more glued to the sound, an effect that is achieved by compressing the signal to subsequently apply a gain to the output of the device, which usually conceals possible interpretation failures by the artist, at least as Dynamic control refers. A compressor is highly recommended (and with certain musical styles, indispensable) for when using an electric bass. The slapping effect (hitting the strings with the finger) produces extremely high output peaks (20 dB or 10 times more than normal), which at low output levels generate distortion, and at high volumes (as in recitals) they can cause serious damage to the amplifier, and even the speaker (an excess of “excursion” can cause the speaker to tear from its suspension). Even in the (theoretical) case of a musical system with an infinite dynamic range, the difference, auditory speaking, using or not the compressor is imperceptible. Its use is also very frequent in voices, since not all singers use the appropriate technique so the signal level varies constantly.
-It is widely used in broadcasting, to improve the speaker’s diction.
-Compress during mastering improves the sound definition of the final mix.
-To protect the equipment (speakers).
