2021년 2월 17일 (수) 08:22에 Pythagoras0님의 편집

2021-02-17T08:22:36Z

Pythagoras0: /* 메타데이터 */ 새 문단

2020-12-26T12:24:49Z

메타데이터: 새 문단

Pythagoras0: /* 노트 */ 새 문단

2020-12-21T08:40:40Z

노트: 새 문단

새 문서

== 노트 ==

===위키데이터===
* ID : [https://www.wikidata.org/wiki/Q2493 Q2493]
===말뭉치===
# Data compression may be lossless (exact) or lossy (inexact).<ref name="ref_734db696">[https://www.britannica.com/technology/data-compression Data compression | computing]</ref>
# Lossless compression can be reversed to yield the original data, while lossy compression loses detail or introduces small errors upon reversal.<ref name="ref_734db696" />
# Lossy compression extends these techniques by removing detail.<ref name="ref_734db696" />
# This technique, as well as fractal techniques, can achieve excellent compression ratios.<ref name="ref_734db696" />
# One data compression technique that is extremely useful with data sets containing large amounts of redundant information is run length encoding (RLE).<ref name="ref_b03b14c2">[https://www.sciencedirect.com/topics/computer-science/data-compression Data Compression - an overview]</ref>
# 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.<ref name="ref_b03b14c2" />
# RLE is often used by many general-purpose data compression software products.<ref name="ref_b03b14c2" />
# Typical values of compression provided by compact are: text (38%), Pascal source (43%), C source (36%) and binary (19%).<ref name="ref_abc485b1">[https://www.ics.uci.edu/~dan/pubs/DataCompression.html Data Compression]</ref>
# 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.<ref name="ref_abc485b1" />
# Much of the available literature on data compression approaches the topic from the point of view of data transmission.<ref name="ref_abc485b1" />
# As noted earlier, data compression is of value in data storage as well.<ref name="ref_abc485b1" />
# 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.<ref name="ref_e858e4f5">[https://www.netmotionsoftware.com/blog/connectivity/how-does-data-compression-work How does data compression work?]</ref>
# Lossy compression reduces file size by removing unnecessary bits of information.<ref name="ref_e858e4f5" />
# Instead, MP3 lossy compression removes sounds that humans can’t hear.<ref name="ref_e858e4f5" />
# The more heavily a file is compressed with lossy compression, the more noticeable the reduction in quality becomes.<ref name="ref_e858e4f5" />
# Lossless compression is a class of data compression algorithms that allows the original data to be perfectly reconstructed from the compressed data.<ref name="ref_f817b338">[https://en.wikipedia.org/wiki/Lossless_compression Lossless compression]</ref>
# By operation of the pigeonhole principle, no lossless compression algorithm can efficiently compress all possible data.<ref name="ref_f817b338" />
# Lossless data compression is used in many applications.<ref name="ref_f817b338" />
# 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.<ref name="ref_f817b338" />
# Data compression is the process of encoding, restructuring or otherwise modifying data in order to reduce its size.<ref name="ref_2b5895ea">[https://www.barracuda.com/glossary/data-compression What is Data Compression?]</ref>
# Compression is done by a program that uses functions or an algorithm to effectively discover how to reduce the size of the data.<ref name="ref_2b5895ea" />
# A good example of this often occurs with image compression.<ref name="ref_2b5895ea" />
# For data transmission, compression can be run on the content or on the entire transmission.<ref name="ref_2b5895ea" />
# Definition - What does Data Compression mean?<ref name="ref_dac329da">[https://www.techopedia.com/definition/884/data-compression Definition from Techopedia]</ref>
# Lossless compression reduces bits by identifying and eliminating statistical redundancy.<ref name="ref_1eaf1ac6">[https://en.wikipedia.org/wiki/Data_compression Data compression]</ref>
# No information is lost in lossless compression.<ref name="ref_1eaf1ac6" />
# The process of reducing the size of a data file is often referred to as data compression.<ref name="ref_1eaf1ac6" />
# Compression is useful because it reduces resources required to store and transmit data.<ref name="ref_1eaf1ac6" />
# Data compression is a reduction in the number of bits needed to represent data.<ref name="ref_c36a5d0c">[https://searchstorage.techtarget.com/definition/compression Definition from WhatIs.com]</ref>
# 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.<ref name="ref_c36a5d0c" />
# Data compression can reduce a text file to 50% or a significantly higher percentage of its original size.<ref name="ref_c36a5d0c" />
# For data transmission, compression can be performed on the data content or on the entire transmission unit, including header data.<ref name="ref_c36a5d0c" />
# Data compression is particularly useful in communications because it enables devices to transmit or store the same amount of data in fewer bits.<ref name="ref_d743c034">[https://www.webopedia.com/definitions/data-compression/ What is Data Compression?]</ref>
# There are a variety of data compression techniques, but only a few have been standardized.<ref name="ref_d743c034" />
# 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).<ref name="ref_d743c034" />
# Data compression is also widely used in backup utilities, spreadsheet applications, and database management systems.<ref name="ref_d743c034" />
# Then I used gzip, bzip2, and zip compression on it.<ref name="ref_ef89c0f4">[https://towardsdatascience.com/winning-the-data-compression-game-af145363ae49 Winning the Data Compression Game]</ref>
# In each case, I supplied whatever command line options produced the most aggressive data compression possible.<ref name="ref_ef89c0f4" />
# We divide 1648 by 3624 to find that the compression ratio is about 45%.<ref name="ref_ef89c0f4" />
# I knew I could do better than a 72% compression ratio.<ref name="ref_ef89c0f4" />
# 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.<ref name="ref_bc58f4c5">[https://docs.python.org/3/library/archiving.html Data Compression and Archiving — Python 3.9.1 documentation]</ref>
# The aim of this work is to study the combination of compression and encryption techniques in digital documents.<ref name="ref_a2ea354a">[https://www.hindawi.com/journals/scn/2018/9591768/ Efficient Compression and Encryption for Digital Data Transmission]</ref>
# Data compression has been one branch of computer science that made this digital revolution possible.<ref name="ref_a2ea354a" />
# The aim of this work is to study the combination of compression and encryption techniques on digital documents.<ref name="ref_a2ea354a" />
# There are therefore two interesting questions to be posed: What is the cost of encryption, in terms of file size, after performing compression?<ref name="ref_a2ea354a" />
# Compression IP is used to put more data into a given fiber or microwave “link” in wireless systems.<ref name="ref_26b0668a">[https://www.renesas.com/us/en/products/interface-connectivity/data-compression IQ Data Compression IP]</ref>
# Using Compression a higher data rate can be transmitted on lower speed links which are generally cheaper.<ref name="ref_26b0668a" />
# 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).<ref name="ref_26b0668a" />
# 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.<ref name="ref_26b0668a" />
# For a list of compression methods, see codec examples .<ref name="ref_548975f6">[https://www.pcmag.com/encyclopedia/term/data-compression Definition of data compression]</ref>
# It merely looks for repeatable patterns of 0s and 1s, and the more patterns, the higher the compression ratio.<ref name="ref_548975f6" />
# Vitis™ Data Compression library is a performance-optimized library to accelerate the Lempel-Ziv (LZ) data compression and decompression algorithms on Xilinx Accelerator cards.<ref name="ref_c0633f74">[https://www.xilinx.com/products/design-tools/vitis/vitis-libraries/vitis-data-compression.html Vitis Data Compression Library]</ref>
# 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.<ref name="ref_c0633f74" />
# 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.<ref name="ref_c0633f74" />
# CZ algorithm uses a parallel pipeline, mixes the coding of compression and encryption, and supports the data window up to 1 TB (or larger).<ref name="ref_de5fd272">[https://www.hindawi.com/journals/js/2017/4209397/ Parallel Algorithm for Wireless Data Compression and Encryption]</ref>
# Moreover, CZ algorithm can encrypt the big data as a chaotic cryptosystem which will not decrease the compression speed.<ref name="ref_de5fd272" />
# 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.<ref name="ref_de5fd272" />
# Data compression is a smart way to speed up the wireless network transportation, and data encryption can protect the transporting information.<ref name="ref_de5fd272" />
# Data compression is the general term for the various algorithms and programs developed to address this problem.<ref name="ref_0b89edad">[https://www.dspguide.com/ch27.htm Data Compression]</ref>
# A compression program is used to convert data from an easy-to-use format to one optimized for compactness.<ref name="ref_0b89edad" />
# We examine five techniques for data compression in this chapter.<ref name="ref_0b89edad" />
# To make this happen, we developed an effective data compression technique by cleverly bucketing our data.<ref name="ref_5bd73ed3">[https://netflixtechblog.com/data-compression-for-large-scale-streaming-experimentation-c20bfab8b9ce Data Compression for Large-Scale Streaming Experimentation]</ref>
# Compression is used in many statistical applications, but why is it so valuable for Quality of Experience metrics?<ref name="ref_5bd73ed3" />
# In practice, these compression techniques reduce the number of rows in the dataset by a factor of 1000 while maintaining accurate results!<ref name="ref_5bd73ed3" />
# The development of an effective data compression strategy completely changed the impact of our statistical tools for streaming experimentation at Netflix.<ref name="ref_5bd73ed3" />
# 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.<ref name="ref_b585cf56">[https://engineering.fb.com/2016/08/31/core-data/smaller-and-faster-data-compression-with-zstandard/ Smaller and faster data compression with Zstandard]</ref>
# Over the years, other algorithms have offered either better compression or faster compression, but rarely both.<ref name="ref_b585cf56" />
# 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.<ref name="ref_b585cf56" />
# 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.<ref name="ref_b585cf56" />
# Compression is the process used to reduce the physical size of a block of information.<ref name="ref_6cfe16d0">[https://www.fileformat.info/mirror/egff/ch09_01.htm GFF CD-ROM/Internet Edition: Chapter 9. Data Compression]</ref>
# We also might use compression to fit larger images in a block of memory of a given size.<ref name="ref_6cfe16d0" />
# 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.<ref name="ref_6cfe16d0" />
# Data compression is a type of data encoding, and one that is used to reduce the size of data.<ref name="ref_6cfe16d0" />
# We introduce Bit-Swap, a scalable and effective lossless data compression technique based on deep learning.<ref name="ref_dddffd3a">[https://bair.berkeley.edu/blog/2019/09/19/bit-swap/ A Deep Learning Approach to Data Compression]</ref>
# It extends previous work on practical compression with latent variable models, based on bits-back coding and asymmetric numeral systems.<ref name="ref_dddffd3a" />
# We’re releasing code for the method and optimized models such that people can explore and advance this line of modern compression ideas.<ref name="ref_dddffd3a" />
# We also release a demo and a pre-trained model for Bit-Swap image compression and decompression on your own image.<ref name="ref_dddffd3a" />
# 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.<ref name="ref_5ca9f1e2">[https://www.f5.com/services/resources/white-papers/understanding-advanced-data-compression Understanding Advanced Data Compression]</ref>
# Others techniques, such as disk-based compression systems, can store as much as 1 terabyte of data.<ref name="ref_5ca9f1e2" />
# All modern, dictionary-based compression systems leverage uneven distribution by storing more frequently accessed data and discarding less frequently accessed data.<ref name="ref_5ca9f1e2" />
# For example, while gzip stores only 64 KB of history, it averages approximately 64 percent compression.<ref name="ref_5ca9f1e2" />
# Data compression is used everywhere.<ref name="ref_eaa09321">[https://teachcomputerscience.com/data-compression/ Data Compression]</ref>
# Without data compression a 3-minute song would be over 100Mb in size, while a 10-minute video would be over 1Gb in size.<ref name="ref_eaa09321" />
# Data compression shrinks big files into much smaller ones.<ref name="ref_eaa09321" />
# Data compression can be expressed as a decrease in the number of bits required to illustrate data.<ref name="ref_eaa09321" />
# Data compression is the process of encoding files and data like text, audio, graphics, images, etc.<ref name="ref_18dac1cb">[https://www.engineersgarage.com/how_to/how-data-compression-works/ How Data Compression works]</ref>
# In order to retrieve the actual information from the compressed file the algorithm for the both compression and uncompression must be same.<ref name="ref_18dac1cb" />
# Let’s take an example of popular compression software WinZip.<ref name="ref_18dac1cb" />
# 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.<ref name="ref_725ca069">[https://sidechannel.tempestsi.com/a-brief-analysis-of-data-compression-security-issues-2d6368782e31 A brief analysis of data compression security issues]</ref>
# Generally speaking, a compression algorithm has as its main objective the reduction of space required to store the same amount of information.<ref name="ref_725ca069" />
# In an HTTP request, the header field, Accept-Encoding, allows the client to explicitly use some coding in the communication (usually the compression itself).<ref name="ref_725ca069" />
# 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.<ref name="ref_725ca069" />
# Lossless compression is ideal for compressing text or numeric files where a loss of data is unacceptable.<ref name="ref_f04b219a">[https://www.computerscience.gcse.guru/theory/data-compression Computer Science GCSE GURU]</ref>
# Our MP4 Video Compression page explains more about MP4 compression.<ref name="ref_f04b219a" />
# Voice compression technology is widely used in digital communication systems such as wireless systems, VoIP, and video conference technology.<ref name="ref_2625cc77">[https://noesis-tech.com/voice-data-compression/ Voice & Data Compression]</ref>
# Voice compression reduces data redundancy and thus eases bandwidth requirements.<ref name="ref_2625cc77" />
# 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.<ref name="ref_2625cc77" />
# In addition, Noesis Technologies offers a proprietary implementation of Huffman block differential lossless data compression algorithm.<ref name="ref_2625cc77" />
# These problems can be overcome by using compression .<ref name="ref_90ab8e8f">[https://www.bbc.co.uk/bitesize/guides/zd88jty/revision/9 Fundamentals of data representation]</ref>
# For sound, lossy compression may remove sounds outside the human range of hearing that were nevertheless picked up during recording.<ref name="ref_90ab8e8f" />
# This work demonstrates that, with several typical compression algorithms, there is a actually a net energy increase when compression is applied before transmission.<ref name="ref_cb1e08fd">[https://dl.acm.org/doi/10.1145/1151690.1151692 Energy-aware lossless data compression]</ref>
# 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.<ref name="ref_3c3ee883">[https://link.springer.com/article/10.1007/s11227-018-2478-3 Performance analysis of data compression algorithms for heterogeneous architecture through parallel approach]</ref>
# Data compression is a technique which represents an information, images, video files in a compressed or in a compact format.<ref name="ref_3c3ee883" />
# 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.<ref name="ref_3c3ee883" />
# This paper presents an efficient parallel approach to reduce execution time for compression algorithms.<ref name="ref_3c3ee883" />
# Compression is the process of encoding data more efficiently to achieve a reduction in file size.<ref name="ref_080e65eb">[https://www.2brightsparks.com/resources/articles/data-compression.html The Basic Principles of Data Compression]</ref>
# One type of compression available is referred to as lossless compression.<ref name="ref_080e65eb" />
# This is essential to data compression as the file would be corrupted and unusable should data be lost.<ref name="ref_080e65eb" />
# Lossless compression algorithms use statistic modelling techniques to reduce repetitive information in a file.<ref name="ref_080e65eb" />
# We show that with several typical compression tools, there is a net energy increase when compression is applied before transmission.<ref name="ref_2e8408e2">[https://www.usenix.org/legacyurl/energy-aware-lossless-data-compression Energy Aware Lossless Data Compression]</ref>
# We also explore the fact that, for many usage models, compression and decompression need not be performed by the same algorithm.<ref name="ref_2e8408e2" />
# 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%.<ref name="ref_2e8408e2" />
# Fidelity can not be sacrificed to reduce energy as is done in related work on lossy compression.<ref name="ref_2e8408e2" />
# It also offers a special mode for small data, called dictionary compression.<ref name="ref_889f8d43">[https://facebook.github.io/zstd/ Real-time data compression algorithm]</ref>
# The reference library offers a very wide range of speed / compression trade-off, and is backed by an extremely fast decoder (see benchmarks below).<ref name="ref_889f8d43" />
===소스===
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← 이전 판		2020년 12월 26일 (토) 12:24 판
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		+	* ID : [https://www.wikidata.org/wiki/Q2493 Q2493]

데이터 압축 - 편집 역사

2021년 2월 17일 (수) 08:22에 Pythagoras0님의 편집

Pythagoras0: /* 메타데이터 */ 새 문단

Pythagoras0: /* 노트 */ 새 문단