Evaluation of Lossless and Lossy Algorithms for the Compression of Scientific Datasets in Netcdf-4 Or HDF5 files
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Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1 -
Package 'Brotli'
Package ‘brotli’ May 13, 2018 Type Package Title A Compression Format Optimized for the Web Version 1.2 Description A lossless compressed data format that uses a combination of the LZ77 algorithm and Huffman coding. Brotli is similar in speed to deflate (gzip) but offers more dense compression. License MIT + file LICENSE URL https://tools.ietf.org/html/rfc7932 (spec) https://github.com/google/brotli#readme (upstream) http://github.com/jeroen/brotli#read (devel) BugReports http://github.com/jeroen/brotli/issues VignetteBuilder knitr, R.rsp Suggests spelling, knitr, R.rsp, microbenchmark, rmarkdown, ggplot2 RoxygenNote 6.0.1 Language en-US NeedsCompilation yes Author Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>), Google, Inc [aut, cph] (Brotli C++ library) Maintainer Jeroen Ooms <[email protected]> Repository CRAN Date/Publication 2018-05-13 20:31:43 UTC R topics documented: brotli . .2 Index 4 1 2 brotli brotli Brotli Compression Description Brotli is a compression algorithm optimized for the web, in particular small text documents. Usage brotli_compress(buf, quality = 11, window = 22) brotli_decompress(buf) Arguments buf raw vector with data to compress/decompress quality value between 0 and 11 window log of window size Details Brotli decompression is at least as fast as for gzip while significantly improving the compression ratio. The price we pay is that compression is much slower than gzip. Brotli is therefore most effective for serving static content such as fonts and html pages. For binary (non-text) data, the compression ratio of Brotli usually does not beat bz2 or xz (lzma), however decompression for these algorithms is too slow for browsers in e.g. -
A Survey Paper on Different Speech Compression Techniques
Vol-2 Issue-5 2016 IJARIIE-ISSN (O)-2395-4396 A Survey Paper on Different Speech Compression Techniques Kanawade Pramila.R1, Prof. Gundal Shital.S2 1 M.E. Electronics, Department of Electronics Engineering, Amrutvahini College of Engineering, Sangamner, Maharashtra, India. 2 HOD in Electronics Department, Department of Electronics Engineering , Amrutvahini College of Engineering, Sangamner, Maharashtra, India. ABSTRACT This paper describes the different types of speech compression techniques. Speech compression can be divided into two main types such as lossless and lossy compression. This survey paper has been written with the help of different types of Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression etc. Compression is nothing but reducing size of data with considering memory size. Speech compression means voiced signal compress for different application such as high quality database of speech signals, multimedia applications, music database and internet applications. Today speech compression is very useful in our life. The main purpose or aim of speech compression is to compress any type of audio that is transfer over the communication channel, because of the limited channel bandwidth and data storage capacity and low bit rate. The use of lossless and lossy techniques for speech compression means that reduced the numbers of bits in the original information. By the use of lossless data compression there is no loss in the original information but while using lossy data compression technique some numbers of bits are loss. Keyword: - Bit rate, Compression, Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression. 1. INTRODUCTION -1 Speech compression is use in the encoding system. -
ROOT I/O Compression Improvements for HEP Analysis
EPJ Web of Conferences 245, 02017 (2020) https://doi.org/10.1051/epjconf/202024502017 CHEP 2019 ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, enhancing it by improving its performance and interaction with other data analysis ecosys- tems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased over the last couple of years. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly, because there are significant trade-offs between the increased CPU cost for reading and writing files and the reduces storage space. 1 Introduction In the past years, Large Hadron Collider (LHC) experiments are managing about an exabyte of storage for analysis purposes, approximately half of which is stored on tape storages for archival purposes, and half is used for traditional disk storage. Meanwhile for High Lumi- nosity Large Hadron Collider (HL-LHC) storage requirements per year are expected to be increased by a factor of 10 [1]. -
Download Media Player Codec Pack Version 4.1 Media Player Codec Pack
download media player codec pack version 4.1 Media Player Codec Pack. Description: In Microsoft Windows 10 it is not possible to set all file associations using an installer. Microsoft chose to block changes of file associations with the introduction of their Zune players. Third party codecs are also blocked in some instances, preventing some files from playing in the Zune players. A simple workaround for this problem is to switch playback of video and music files to Windows Media Player manually. In start menu click on the "Settings". In the "Windows Settings" window click on "System". On the "System" pane click on "Default apps". On the "Choose default applications" pane click on "Films & TV" under "Video Player". On the "Choose an application" pop up menu click on "Windows Media Player" to set Windows Media Player as the default player for video files. Footnote: The same method can be used to apply file associations for music, by simply clicking on "Groove Music" under "Media Player" instead of changing Video Player in step 4. Media Player Codec Pack Plus. Codec's Explained: A codec is a piece of software on either a device or computer capable of encoding and/or decoding video and/or audio data from files, streams and broadcasts. The word Codec is a portmanteau of ' co mpressor- dec ompressor' Compression types that you will be able to play include: x264 | x265 | h.265 | HEVC | 10bit x265 | 10bit x264 | AVCHD | AVC DivX | XviD | MP4 | MPEG4 | MPEG2 and many more. File types you will be able to play include: .bdmv | .evo | .hevc | .mkv | .avi | .flv | .webm | .mp4 | .m4v | .m4a | .ts | .ogm .ac3 | .dts | .alac | .flac | .ape | .aac | .ogg | .ofr | .mpc | .3gp and many more. -
Divx Codec Package
Divx codec package Videos. How To Use DivX Mux GUI · How to Stream DivX Plus HD (MKV) files to your Xbox · more. Guides. There are no guides available. Search FAQs. Téléchargement gratuit. Inclut DivX Codec et tout ce dont vous avez besoin pour lire les fichiers DivX, AVI ou MKV dans n'importe quel lecteur multimédia. Kostenloser Download. Umfasst DivX Codec und alles, was Du zur Wiedergabe von DivX-, AVI- oder MKV-Dateien in einem beliebigen Media-Player brauchst. H codecs compress digital video files so that they only use half the space of MPEG-2, to deliver the same quality video. An H encoder delivers. Download grátis. Inclui DivX Codec e tudo o mais de que você precisa para reproduzir arquivos DivX, AVI ou MKV em qualquer player de mídia. Free video software downloads to play & stream DivX (AVI) & DivX Plus HD (MKV) video. Find devices to play DivX video and Hollywood movies in DivX format. You can do it all in one go and be ready for any video format that comes your way. Codec Pack All-in-1 includes: DivX ; XviD Codec Media Player Codec Pack for Microsoft Windows, 10, , 8, 7, Vista, XP, , x | h | HEVC | 10bit x | x | h | AVCHD | AVC | DivX | XviD. They feature improved HEVC and AVC decoders for better stability and the DivX codec pack has been removed for consistency around the. Codec Pack All in 1, free and safe download. Codec Pack All in 1 latest version: A free Video program for Windows. Codec Pack All in 1 is a good, free Windows. -
Arxiv:2004.10531V1 [Cs.OH] 8 Apr 2020
ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, increasing per- formance and enhancing it and improving its interaction with other data analy- sis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are sig- nificant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space. 1 Introduction In the past years LHC experiments are commissioned and now manages about an exabyte of storage for analysis purposes, approximately half of which is used for archival purposes, and half is used for traditional disk storage. Meanwhile for HL-LHC storage requirements per year are expected to be increased by factor 10 [1]. arXiv:2004.10531v1 [cs.OH] 8 Apr 2020 Looking at these predictions, we would like to state that storage will remain one of the major cost drivers and at the same time the bottlenecks for HEP computing. -
Installation Manual
CX-20 Installation manual ENABLING BRIGHT OUTCOMES Barco NV Beneluxpark 21, 8500 Kortrijk, Belgium www.barco.com/en/support www.barco.com Registered office: Barco NV President Kennedypark 35, 8500 Kortrijk, Belgium www.barco.com/en/support www.barco.com Copyright © All rights reserved. No part of this document may be copied, reproduced or translated. It shall not otherwise be recorded, transmitted or stored in a retrieval system without the prior written consent of Barco. Trademarks Brand and product names mentioned in this manual may be trademarks, registered trademarks or copyrights of their respective holders. All brand and product names mentioned in this manual serve as comments or examples and are not to be understood as advertising for the products or their manufacturers. Trademarks USB Type-CTM and USB-CTM are trademarks of USB Implementers Forum. HDMI Trademark Notice The terms HDMI, HDMI High Definition Multimedia Interface, and the HDMI Logo are trademarks or registered trademarks of HDMI Licensing Administrator, Inc. Product Security Incident Response As a global technology leader, Barco is committed to deliver secure solutions and services to our customers, while protecting Barco’s intellectual property. When product security concerns are received, the product security incident response process will be triggered immediately. To address specific security concerns or to report security issues with Barco products, please inform us via contact details mentioned on https://www.barco.com/psirt. To protect our customers, Barco does not publically disclose or confirm security vulnerabilities until Barco has conducted an analysis of the product and issued fixes and/or mitigations. Patent protection Please refer to www.barco.com/about-barco/legal/patents Guarantee and Compensation Barco provides a guarantee relating to perfect manufacturing as part of the legally stipulated terms of guarantee. -
The Basic Principles of Data Compression
The Basic Principles of Data Compression Author: Conrad Chung, 2BrightSparks Introduction Internet users who download or upload files from/to the web, or use email to send or receive attachments will most likely have encountered files in compressed format. In this topic we will cover how compression works, the advantages and disadvantages of compression, as well as types of compression. What is Compression? Compression is the process of encoding data more efficiently to achieve a reduction in file size. One type of compression available is referred to as lossless compression. This means the compressed file will be restored exactly to its original state with no loss of data during the decompression process. This is essential to data compression as the file would be corrupted and unusable should data be lost. Another compression category which will not be covered in this article is “lossy” compression often used in multimedia files for music and images and where data is discarded. Lossless compression algorithms use statistic modeling techniques to reduce repetitive information in a file. Some of the methods may include removal of spacing characters, representing a string of repeated characters with a single character or replacing recurring characters with smaller bit sequences. Advantages/Disadvantages of Compression Compression of files offer many advantages. When compressed, the quantity of bits used to store the information is reduced. Files that are smaller in size will result in shorter transmission times when they are transferred on the Internet. Compressed files also take up less storage space. File compression can zip up several small files into a single file for more convenient email transmission. -
Implementing Compression on Distributed Time Series Database
Implementing compression on distributed time series database Michael Burman School of Science Thesis submitted for examination for the degree of Master of Science in Technology. Espoo 05.11.2017 Supervisor Prof. Kari Smolander Advisor Mgr. Jiri Kremser Aalto University, P.O. BOX 11000, 00076 AALTO www.aalto.fi Abstract of the master’s thesis Author Michael Burman Title Implementing compression on distributed time series database Degree programme Major Computer Science Code of major SCI3042 Supervisor Prof. Kari Smolander Advisor Mgr. Jiri Kremser Date 05.11.2017 Number of pages 70+4 Language English Abstract Rise of microservices and distributed applications in containerized deployments are putting increasing amount of burden to the monitoring systems. They push the storage requirements to provide suitable performance for large queries. In this paper we present the changes we made to our distributed time series database, Hawkular-Metrics, and how it stores data more effectively in the Cassandra. We show that using our methods provides significant space savings ranging from 50 to 95% reduction in storage usage, while reducing the query times by over 90% compared to the nominal approach when using Cassandra. We also provide our unique algorithm modified from Gorilla compression algorithm that we use in our solution, which provides almost three times the throughput in compression with equal compression ratio. Keywords timeseries compression performance storage Aalto-yliopisto, PL 11000, 00076 AALTO www.aalto.fi Diplomityön tiivistelmä Tekijä Michael Burman Työn nimi Pakkausmenetelmät hajautetussa aikasarjatietokannassa Koulutusohjelma Pääaine Computer Science Pääaineen koodi SCI3042 Työn valvoja ja ohjaaja Prof. Kari Smolander Päivämäärä 05.11.2017 Sivumäärä 70+4 Kieli Englanti Tiivistelmä Hajautettujen järjestelmien yleistyminen on aiheuttanut valvontajärjestelmissä tiedon määrän kasvua, sillä aikasarjojen määrä on kasvanut ja niihin talletetaan useammin tietoa. -
Lossless Compression of Audio Data
CHAPTER 12 Lossless Compression of Audio Data ROBERT C. MAHER OVERVIEW Lossless data compression of digital audio signals is useful when it is necessary to minimize the storage space or transmission bandwidth of audio data while still maintaining archival quality. Available techniques for lossless audio compression, or lossless audio packing, generally employ an adaptive waveform predictor with a variable-rate entropy coding of the residual, such as Huffman or Golomb-Rice coding. The amount of data compression can vary considerably from one audio waveform to another, but ratios of less than 3 are typical. Several freeware, shareware, and proprietary commercial lossless audio packing programs are available. 12.1 INTRODUCTION The Internet is increasingly being used as a means to deliver audio content to end-users for en tertainment, education, and commerce. It is clearly advantageous to minimize the time required to download an audio data file and the storage capacity required to hold it. Moreover, the expec tations of end-users with regard to signal quality, number of audio channels, meta-data such as song lyrics, and similar additional features provide incentives to compress the audio data. 12.1.1 Background In the past decade there have been significant breakthroughs in audio data compression using lossy perceptual coding [1]. These techniques lower the bit rate required to represent the signal by establishing perceptual error criteria, meaning that a model of human hearing perception is Copyright 2003. Elsevier Science (USA). 255 AU rights reserved. 256 PART III / APPLICATIONS used to guide the elimination of excess bits that can be either reconstructed (redundancy in the signal) orignored (inaudible components in the signal). -
The H.264 Advanced Video Coding (AVC) Standard
Whitepaper: The H.264 Advanced Video Coding (AVC) Standard What It Means to Web Camera Performance Introduction A new generation of webcams is hitting the market that makes video conferencing a more lifelike experience for users, thanks to adoption of the breakthrough H.264 standard. This white paper explains some of the key benefits of H.264 encoding and why cameras with this technology should be on the shopping list of every business. The Need for Compression Today, Internet connection rates average in the range of a few megabits per second. While VGA video requires 147 megabits per second (Mbps) of data, full high definition (HD) 1080p video requires almost one gigabit per second of data, as illustrated in Table 1. Table 1. Display Resolution Format Comparison Format Horizontal Pixels Vertical Lines Pixels Megabits per second (Mbps) QVGA 320 240 76,800 37 VGA 640 480 307,200 147 720p 1280 720 921,600 442 1080p 1920 1080 2,073,600 995 Video Compression Techniques Digital video streams, especially at high definition (HD) resolution, represent huge amounts of data. In order to achieve real-time HD resolution over typical Internet connection bandwidths, video compression is required. The amount of compression required to transmit 1080p video over a three megabits per second link is 332:1! Video compression techniques use mathematical algorithms to reduce the amount of data needed to transmit or store video. Lossless Compression Lossless compression changes how data is stored without resulting in any loss of information. Zip files are losslessly compressed so that when they are unzipped, the original files are recovered.