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  1. Data compression may be lossless (exact) or lossy (inexact).[1]
  2. Lossless compression can be reversed to yield the original data, while lossy compression loses detail or introduces small errors upon reversal.[1]
  3. Lossy compression extends these techniques by removing detail.[1]
  4. This technique, as well as fractal techniques, can achieve excellent compression ratios.[1]
  5. One data compression technique that is extremely useful with data sets containing large amounts of redundant information is run length encoding (RLE).[2]
  6. Since RLE is a lossless compression method, it can also be applied to typical data acquisition data sets if they contain large amounts of redundant information.[2]
  7. RLE is often used by many general-purpose data compression software products.[2]
  8. Typical values of compression provided by compact are: text (38%), Pascal source (43%), C source (36%) and binary (19%).[3]
  9. Cormack reports that data compression programs based on Huffman coding (Section 3.2) reduced the size of a large student-record database by 42.1% when only some of the information was compressed.[3]
  10. Much of the available literature on data compression approaches the topic from the point of view of data transmission.[3]
  11. As noted earlier, data compression is of value in data storage as well.[3]
  12. Data compression is a process in which the size of a file is reduced by re-encoding the file data to use fewer bits of storage than the original file.[4]
  13. Lossy compression reduces file size by removing unnecessary bits of information.[4]
  14. Instead, MP3 lossy compression removes sounds that humans can’t hear.[4]
  15. The more heavily a file is compressed with lossy compression, the more noticeable the reduction in quality becomes.[4]
  16. Lossless compression is a class of data compression algorithms that allows the original data to be perfectly reconstructed from the compressed data.[5]
  17. By operation of the pigeonhole principle, no lossless compression algorithm can efficiently compress all possible data.[5]
  18. Lossless data compression is used in many applications.[5]
  19. 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.[5]
  20. Data compression is the process of encoding, restructuring or otherwise modifying data in order to reduce its size.[6]
  21. Compression is done by a program that uses functions or an algorithm to effectively discover how to reduce the size of the data.[6]
  22. A good example of this often occurs with image compression.[6]
  23. For data transmission, compression can be run on the content or on the entire transmission.[6]
  24. Definition - What does Data Compression mean?[7]
  25. Lossless compression reduces bits by identifying and eliminating statistical redundancy.[8]
  26. No information is lost in lossless compression.[8]
  27. The process of reducing the size of a data file is often referred to as data compression.[8]
  28. Compression is useful because it reduces resources required to store and transmit data.[8]
  29. Data compression is a reduction in the number of bits needed to represent data.[9]
  30. How compression works Compression is performed by a program that uses a formula or algorithm to determine how to shrink the size of the data.[9]
  31. Data compression can reduce a text file to 50% or a significantly higher percentage of its original size.[9]
  32. For data transmission, compression can be performed on the data content or on the entire transmission unit, including header data.[9]
  33. Data compression is particularly useful in communications because it enables devices to transmit or store the same amount of data in fewer bits.[10]
  34. There are a variety of data compression techniques, but only a few have been standardized.[10]
  35. The CCITT has defined a standard data compression technique for transmitting faxes (Group 3 standard) and a compression standard for data communications through modems (CCITT V.42bis).[10]
  36. Data compression is also widely used in backup utilities, spreadsheet applications, and database management systems.[10]
  37. Then I used gzip, bzip2, and zip compression on it.[11]
  38. In each case, I supplied whatever command line options produced the most aggressive data compression possible.[11]
  39. We divide 1648 by 3624 to find that the compression ratio is about 45%.[11]
  40. I knew I could do better than a 72% compression ratio.[11]
  41. The modules described in this chapter support data compression with the zlib, gzip, bzip2 and lzma algorithms, and the creation of ZIP- and tar-format archives.[12]
  42. The aim of this work is to study the combination of compression and encryption techniques in digital documents.[13]
  43. Data compression has been one branch of computer science that made this digital revolution possible.[13]
  44. The aim of this work is to study the combination of compression and encryption techniques on digital documents.[13]
  45. There are therefore two interesting questions to be posed: What is the cost of encryption, in terms of file size, after performing compression?[13]
  46. Compression IP is used to put more data into a given fiber or microwave “link” in wireless systems.[14]
  47. Using Compression a higher data rate can be transmitted on lower speed links which are generally cheaper.[14]
  48. Compression is used to compress data in wireless systems on the link between the Remote Radio Unit (RRU) and the Baseband Card (wired over CPRI or CPRI over wireless front haul).[14]
  49. Compression IP makes wireless C-RAN architectures more viable by allowing RRUs to be placed remote from Baseband Pools connected with low cost fiber, saving large amounts of money at the system level.[14]
  50. For a list of compression methods, see codec examples .[15]
  51. It merely looks for repeatable patterns of 0s and 1s, and the more patterns, the higher the compression ratio.[15]
  52. Vitis™ Data Compression library is a performance-optimized library to accelerate the Lempel-Ziv (LZ) data compression and decompression algorithms on Xilinx Accelerator cards.[16]
  53. It is designed as a specialized compression engine, multiple of which can run concurrently on the same Xilinx accelerator card to meet the high-throughput requirements of your algorithms.[16]
  54. You can use the pre-optimized library kernels for LZ4 and Snappy compression/decompression or use the low-level optimized primitives as components while designing your end-to-end accelerated kernel.[16]
  55. CZ algorithm uses a parallel pipeline, mixes the coding of compression and encryption, and supports the data window up to 1 TB (or larger).[17]
  56. Moreover, CZ algorithm can encrypt the big data as a chaotic cryptosystem which will not decrease the compression speed.[17]
  57. The experiment results show that ComZip in 64 b system can get better compression ratio than WinRAR and 7-zip, and it can be faster than 7-zip in the big data compression.[17]
  58. Data compression is a smart way to speed up the wireless network transportation, and data encryption can protect the transporting information.[17]
  59. Data compression is the general term for the various algorithms and programs developed to address this problem.[18]
  60. A compression program is used to convert data from an easy-to-use format to one optimized for compactness.[18]
  61. We examine five techniques for data compression in this chapter.[18]
  62. To make this happen, we developed an effective data compression technique by cleverly bucketing our data.[19]
  63. Compression is used in many statistical applications, but why is it so valuable for Quality of Experience metrics?[19]
  64. In practice, these compression techniques reduce the number of rows in the dataset by a factor of 1000 while maintaining accurate results![19]
  65. The development of an effective data compression strategy completely changed the impact of our statistical tools for streaming experimentation at Netflix.[19]
  66. Software helps with data processing through compression, which encodes information, like text, pictures, and other forms of digital data, using fewer bits than the original.[20]
  67. Over the years, other algorithms have offered either better compression or faster compression, but rarely both.[20]
  68. We're thrilled to announce Zstandard 1.0, a new compression algorithm and implementation designed to scale with modern hardware and compress smaller and faster.[20]
  69. Zstandard combines recent compression breakthroughs, like Finite State Entropy, with a performance-first design — and then optimizes the implementation for the unique properties of modern CPUs.[20]
  70. Compression is the process used to reduce the physical size of a block of information.[21]
  71. We also might use compression to fit larger images in a block of memory of a given size.[21]
  72. You may find when you examine a particular file format specification that the term data encoding is used to refer to algorithms that perform compression.[21]
  73. Data compression is a type of data encoding, and one that is used to reduce the size of data.[21]
  74. We introduce Bit-Swap, a scalable and effective lossless data compression technique based on deep learning.[22]
  75. It extends previous work on practical compression with latent variable models, based on bits-back coding and asymmetric numeral systems.[22]
  76. We’re releasing code for the method and optimized models such that people can explore and advance this line of modern compression ideas.[22]
  77. We also release a demo and a pre-trained model for Bit-Swap image compression and decompression on your own image.[22]
  78. Session layer compression enables a BIG-IP AAM-enabled BIG-IP LTM device to easily find matches in data streams that at Layer 3 might be many bytes apart, but at Layer 5 are contiguous.[23]
  79. Others techniques, such as disk-based compression systems, can store as much as 1 terabyte of data.[23]
  80. All modern, dictionary-based compression systems leverage uneven distribution by storing more frequently accessed data and discarding less frequently accessed data.[23]
  81. For example, while gzip stores only 64 KB of history, it averages approximately 64 percent compression.[23]
  82. Data compression is used everywhere.[24]
  83. Without data compression a 3-minute song would be over 100Mb in size, while a 10-minute video would be over 1Gb in size.[24]
  84. Data compression shrinks big files into much smaller ones.[24]
  85. Data compression can be expressed as a decrease in the number of bits required to illustrate data.[24]
  86. Data compression is the process of encoding files and data like text, audio, graphics, images, etc.[25]
  87. In order to retrieve the actual information from the compressed file the algorithm for the both compression and uncompression must be same.[25]
  88. Let’s take an example of popular compression software WinZip.[25]
  89. This article introduces some of the security issues that surround data compression with later encryption and demonstrates that, in certain cases, it is safer to only encrypt it.[26]
  90. Generally speaking, a compression algorithm has as its main objective the reduction of space required to store the same amount of information.[26]
  91. In an HTTP request, the header field, Accept-Encoding, allows the client to explicitly use some coding in the communication (usually the compression itself).[26]
  92. Below are the results of a test in which we compared the size and response time of two requests, one that uses compression and one that doesn’t use it.[26]
  93. Lossless compression is ideal for compressing text or numeric files where a loss of data is unacceptable.[27]
  94. Our MP4 Video Compression page explains more about MP4 compression.[27]
  95. Voice compression technology is widely used in digital communication systems such as wireless systems, VoIP, and video conference technology.[28]
  96. Voice compression reduces data redundancy and thus eases bandwidth requirements.[28]
  97. Noesis Technologies provides a series of silicon IPs of the most popular voice codecs (G711, G726, G729, CVSD), providing compression rates ranging from 64 kbps down to 8 kbps.[28]
  98. In addition, Noesis Technologies offers a proprietary implementation of Huffman block differential lossless data compression algorithm.[28]
  99. These problems can be overcome by using compression .[29]
  100. For sound, lossy compression may remove sounds outside the human range of hearing that were nevertheless picked up during recording.[29]
  101. This work demonstrates that, with several typical compression algorithms, there is a actually a net energy increase when compression is applied before transmission.[30]
  102. Today, there is a huge demand for data compression due to the need to reduce the transmission time and increase the capacity of data storage.[31]
  103. Data compression is a technique which represents an information, images, video files in a compressed or in a compact format.[31]
  104. There are various data compression techniques which keep information as accurately as possible with the fewest number of bits and send it through communication channel.[31]
  105. This paper presents an efficient parallel approach to reduce execution time for compression algorithms.[31]
  106. Compression is the process of encoding data more efficiently to achieve a reduction in file size.[32]
  107. One type of compression available is referred to as lossless compression.[32]
  108. This is essential to data compression as the file would be corrupted and unusable should data be lost.[32]
  109. Lossless compression algorithms use statistic modelling techniques to reduce repetitive information in a file.[32]
  110. We show that with several typical compression tools, there is a net energy increase when compression is applied before transmission.[33]
  111. We also explore the fact that, for many usage models, compression and decompression need not be performed by the same algorithm.[33]
  112. By choosing the lowest-energy compressor and decompressor on the test platform, rather than using default levels of compression, overall energy to send compressible web data can be reduced 31%.[33]
  113. Fidelity can not be sacrificed to reduce energy as is done in related work on lossy compression.[33]
  114. It also offers a special mode for small data, called dictionary compression.[34]
  115. The reference library offers a very wide range of speed / compression trade-off, and is backed by an extremely fast decoder (see benchmarks below).[34]

소스

  1. ↑ 1.0 1.1 1.2 1.3 Data compression | computing
  2. ↑ 2.0 2.1 2.2 Data Compression - an overview
  3. ↑ 3.0 3.1 3.2 3.3 Data Compression
  4. ↑ 4.0 4.1 4.2 4.3 How does data compression work?
  5. ↑ 5.0 5.1 5.2 5.3 Lossless compression
  6. ↑ 6.0 6.1 6.2 6.3 What is Data Compression?
  7. ↑ Definition from Techopedia
  8. ↑ 8.0 8.1 8.2 8.3 Data compression
  9. ↑ 9.0 9.1 9.2 9.3 Definition from WhatIs.com
  10. ↑ 10.0 10.1 10.2 10.3 What is Data Compression?
  11. ↑ 11.0 11.1 11.2 11.3 Winning the Data Compression Game
  12. ↑ Data Compression and Archiving — Python 3.9.1 documentation
  13. ↑ 13.0 13.1 13.2 13.3 Efficient Compression and Encryption for Digital Data Transmission
  14. ↑ 14.0 14.1 14.2 14.3 IQ Data Compression IP
  15. ↑ 15.0 15.1 Definition of data compression
  16. ↑ 16.0 16.1 16.2 Vitis Data Compression Library
  17. ↑ 17.0 17.1 17.2 17.3 Parallel Algorithm for Wireless Data Compression and Encryption
  18. ↑ 18.0 18.1 18.2 Data Compression
  19. ↑ 19.0 19.1 19.2 19.3 Data Compression for Large-Scale Streaming Experimentation
  20. ↑ 20.0 20.1 20.2 20.3 Smaller and faster data compression with Zstandard
  21. ↑ 21.0 21.1 21.2 21.3 GFF CD-ROM/Internet Edition: Chapter 9. Data Compression
  22. ↑ 22.0 22.1 22.2 22.3 A Deep Learning Approach to Data Compression
  23. ↑ 23.0 23.1 23.2 23.3 Understanding Advanced Data Compression
  24. ↑ 24.0 24.1 24.2 24.3 Data Compression
  25. ↑ 25.0 25.1 25.2 How Data Compression works
  26. ↑ 26.0 26.1 26.2 26.3 A brief analysis of data compression security issues
  27. ↑ 27.0 27.1 Computer Science GCSE GURU
  28. ↑ 28.0 28.1 28.2 28.3 Voice & Data Compression
  29. ↑ 29.0 29.1 Fundamentals of data representation
  30. ↑ Energy-aware lossless data compression
  31. ↑ 31.0 31.1 31.2 31.3 Performance analysis of data compression algorithms for heterogeneous architecture through parallel approach
  32. ↑ 32.0 32.1 32.2 32.3 The Basic Principles of Data Compression
  33. ↑ 33.0 33.1 33.2 33.3 Energy Aware Lossless Data Compression
  34. ↑ 34.0 34.1 Real-time data compression algorithm

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