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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. -
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. -
Data Compression for Remote Laboratories
Paper—Data Compression for Remote Laboratories Data Compression for Remote Laboratories https://doi.org/10.3991/ijim.v11i4.6743 Olawale B. Akinwale Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Lawrence O. Kehinde Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Abstract—Remote laboratories on mobile phones have been around for a few years now. This has greatly improved accessibility of these remote labs to students who cannot afford computers but have mobile phones. When money is a factor however (as is often the case with those who can’t afford a computer), the cost of use of these remote laboratories should be minimized. This work ad- dressed this issue of minimizing the cost of use of the remote lab by making use of data compression for data sent between the user and remote lab. Keywords—Data compression; Lempel-Ziv-Welch; remote laboratory; Web- Sockets 1 Introduction The mobile phone is now the de facto computational device of choice. They are quite powerful, connected devices which are almost always with us. This is so much so that about every important online service has a mobile version or at least a version compatible with mobile phones and tablets (for this paper we would adopt the phrase mobile device to represent mobile phones and tablets). This has caused online labora- tory developers to develop online laboratory solutions which are mobile device- friendly [1, 2, 3, 4, 5, 6, 7, 8]. One of the main benefits of online laboratories is the possibility of using fewer re- sources to serve more people i.e. -
I Introduction
PPM Performance with BWT Complexity: A New Metho d for Lossless Data Compression Michelle E ros California Institute of Technology e [email protected] Abstract This work combines a new fast context-search algorithm with the lossless source co ding mo dels of PPM to achieve a lossless data compression algorithm with the linear context-search complexity and memory of BWT and Ziv-Lemp el co des and the compression p erformance of PPM-based algorithms. Both se- quential and nonsequential enco ding are considered. The prop osed algorithm yields an average rate of 2.27 bits per character bp c on the Calgary corpus, comparing favorably to the 2.33 and 2.34 bp c of PPM5 and PPM and the 2.43 bp c of BW94 but not matching the 2.12 bp c of PPMZ9, which, at the time of this publication, gives the greatest compression of all algorithms rep orted on the Calgary corpus results page. The prop osed algorithm gives an average rate of 2.14 bp c on the Canterbury corpus. The Canterbury corpus web page gives average rates of 1.99 bp c for PPMZ9, 2.11 bp c for PPM5, 2.15 bp c for PPM7, and 2.23 bp c for BZIP2 a BWT-based co de on the same data set. I Intro duction The Burrows Wheeler Transform BWT [1] is a reversible sequence transformation that is b ecoming increasingly p opular for lossless data compression. The BWT rear- ranges the symb ols of a data sequence in order to group together all symb ols that share the same unb ounded history or \context." Intuitively, this op eration is achieved by forming a table in which each row is a distinct cyclic shift of the original data string. -
Dc5m United States Software in English Created at 2016-12-25 16:00
Announcement DC5m United States software in english 1 articles, created at 2016-12-25 16:00 articles set mostly positive rate 10.0 1 3.8 Google’s Brotli Compression Algorithm Lands to Windows Edge Microsoft has announced that its Edge browser has started using Brotli, the compression algorithm that Google open-sourced last year. 2016-12-25 05:00 1KB www.infoq.com Articles DC5m United States software in english 1 articles, created at 2016-12-25 16:00 1 /1 3.8 Google’s Brotli Compression Algorithm Lands to Windows Edge Microsoft has announced that its Edge browser has started using Brotli, the compression algorithm that Google open-sourced last year. Brotli is on by default in the latest Edge build and can be previewed via the Windows Insider Program. It will reach stable status early next year, says Microsoft. Microsoft touts a 20% higher compression ratios over comparable compression algorithms, which would benefit page load times without impacting client-side CPU costs. According to Google, Brotli uses a whole new data format , which makes it incompatible with Deflate but ensures higher compression ratios. In particular, Google says, Brotli is roughly as fast as zlib when decompressing and provides a better compression ratio than LZMA and bzip2 on the Canterbury Corpus. Brotli appears to be especially tuned for the web , that is for offline encoding and online decoding of Web assets, or Android APKs. Google claims a compression ratio improvement of 20–26% over its own Zopfli algorithm, which still provides the best compression ratio of any deflate algorithm. -
Implementing Associative Coder of Buyanovsky (ACB)
Implementing Associative Coder of Buyanovsky (ACB) data compression by Sean Michael Lambert A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Montana State University © Copyright by Sean Michael Lambert (1999) Abstract: In 1994 George Mechislavovich Buyanovsky published a basic description of a new data compression algorithm he called the “Associative Coder of Buyanovsky,” or ACB. The archive program using this idea, which he released in 1996 and updated in 1997, is still one of the best general compression utilities available. Despite this, the ACB algorithm is still barely understood by data compression experts, primarily because Buyanovsky never published a detailed description of it. ACB is a new idea in data compression, merging concepts from existing statistical and dictionary-based algorithms with entirely original ideas. This document presents several variations of the ACB algorithm and the details required to implement a basic version of ACB. IMPLEMENTING ASSOCIATIVE CODER OF BUYANOVSKY (ACB) DATA COMPRESSION by Sean Michael Lambert A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science MONTANA STATE UNIVERSITY-BOZEMAN Bozeman, Montana April 1999 © COPYRIGHT by Sean Michael Lambert 1999 All Rights Reserved ii APPROVAL of a thesis submitted by Sean Michael Lambert This thesis has been read by each member of the thesis committee and has been found to be satisfactory regarding content, English usage, format, citations, bibliographic style, and consistency, and is ready for submission to the College of Graduate Studies. Brendan Mumey U /l! ^ (Signature) Date Approved for the Department of Computer Science J. -
An Analysis of XML Compression Efficiency
An Analysis of XML Compression Efficiency Christopher J. Augeri1 Barry E. Mullins1 Leemon C. Baird III Dursun A. Bulutoglu2 Rusty O. Baldwin1 1Department of Electrical and Computer Engineering Department of Computer Science 2Department of Mathematics and Statistics United States Air Force Academy (USAFA) Air Force Institute of Technology (AFIT) USAFA, Colorado Springs, CO Wright Patterson Air Force Base, Dayton, OH {chris.augeri, barry.mullins}@afit.edu [email protected] {dursun.bulutoglu, rusty.baldwin}@afit.edu ABSTRACT We expand previous XML compression studies [9, 26, 34, 47] by XML simplifies data exchange among heterogeneous computers, proposing the XML file corpus and a combined efficiency metric. but it is notoriously verbose and has spawned the development of The corpus was assembled using guidelines given by developers many XML-specific compressors and binary formats. We present of the Canterbury corpus [3], files often used to assess compressor an XML test corpus and a combined efficiency metric integrating performance. The efficiency metric combines execution speed compression ratio and execution speed. We use this corpus and and compression ratio, enabling simultaneous assessment of these linear regression to assess 14 general-purpose and XML-specific metrics, versus prioritizing one metric over the other. We analyze compressors relative to the proposed metric. We also identify key collected metrics using linear regression models (ANOVA) versus factors when selecting a compressor. Our results show XMill or a simple comparison of means, e.g., X is 20% better than Y. WBXML may be useful in some instances, but a general-purpose compressor is often the best choice. 2. XML OVERVIEW XML has gained much acceptance since first proposed in 1998 by Categories and Subject Descriptors the World-Wide Web Consortium (W3C). -
Improving Compression-Ratio in Backup
Institutionen för systemteknik Department of Electrical Engineering Examensarbete Improving compression ratio in backup Examensarbete utfört i Informationskodning/Bildkodning av Mattias Zeidlitz Författare Mattias Zeidlitz LITH-ISY-EX--12/4588--SE Linköping 2012 TEKNISKA HÖGSKOLAN LINKÖPINGS UNIVERSITET Department of Electrical Engineering Linköpings tekniska högskola Linköping University Institutionen för systemteknik S-581 83 Linköping, Sweden 581 83 Linköping Improving compression-ratio in backup ............................................................................ Examensarbete utfört i Informationskodning/Bildkodning vid Linköpings tekniska högskola av Mattias Zeidlitz ............................................................. LITH-ISY-EX--12/4588--SE Presentationsdatum Institution och avdelning 2012-06-13 Institutionen för systemteknik Publiceringsdatum (elektronisk version) Department of Electrical Engineering Datum då du ämnar publicera exjobbet Språk Typ av publikation ISBN (licentiatavhandling) Svenska Licentiatavhandling ISRN LITH-ISY-EX--12/4588--SE x Annat (ange nedan) x Examensarbete Serietitel (licentiatavhandling) C-uppsats D-uppsats Engelska Rapport Serienummer/ISSN (licentiatavhandling) Antal sidor Annat (ange nedan) 58 URL för elektronisk version http://www.ep.liu.se Publikationens titel Improving compression ratio in backup Författare Mattias Zeidlitz Sammanfattning Denna rapport beskriver ett examensarbete genomfört på Degoo Backup AB i Stockholm under våren 2012. Syftet var att designa en kompressionssvit -
Benefits of Hardware Data Compression in Storage Networks Gerry Simmons, Comtech AHA Tony Summers, Comtech AHA SNIA Legal Notice
Benefits of Hardware Data Compression in Storage Networks Gerry Simmons, Comtech AHA Tony Summers, Comtech AHA SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA. Member companies and individuals may use this material in presentations and literature under the following conditions: Any slide or slides used must be reproduced without modification The SNIA must be acknowledged as source of any material used in the body of any document containing material from these presentations. This presentation is a project of the SNIA Education Committee. Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 2 © 2007 Storage Networking Industry Association. All Rights Reserved. Abstract Benefits of Hardware Data Compression in Storage Networks This tutorial explains the benefits and algorithmic details of lossless data compression in Storage Networks, and focuses especially on data de-duplication. The material presents a brief history and background of Data Compression - a primer on the different data compression algorithms in use today. This primer includes performance data on the specific compression algorithms, as well as performance on different data types. Participants will come away with a good understanding of where to place compression in the data path, and the benefits to be gained by such placement. The tutorial will discuss technological advances in compression and how they affect system level solutions. Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 3 © 2007 Storage Networking Industry Association. All Rights Reserved. Agenda Introduction and Background Lossless Compression Algorithms System Implementations Technology Advances and Compression Hardware Power Conservation and Efficiency Conclusion Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 4 © 2007 Storage Networking Industry Association. -
The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 3296-3414 © Research India Publications. http://www.ripublication.com The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree Evon Abu-Taieh, PhD Information System Technology Faculty, The University of Jordan, Aqaba, Jordan. Abstract tree is presented in the last section of the paper after presenting the 12 main compression algorithms each with a practical This paper presents the pillars of lossless compression example. algorithms, methods and techniques. The paper counted more than 40 compression algorithms. Although each algorithm is The paper first introduces Shannon–Fano code showing its an independent in its own right, still; these algorithms relation to Shannon (1948), Huffman coding (1952), FANO interrelate genealogically and chronologically. The paper then (1949), Run Length Encoding (1967), Peter's Version (1963), presents the genealogy tree suggested by researcher. The tree Enumerative Coding (1973), LIFO (1976), FiFO Pasco (1976), shows the interrelationships between the 40 algorithms. Also, Stream (1979), P-Based FIFO (1981). Two examples are to be the tree showed the chronological order the algorithms came to presented one for Shannon-Fano Code and the other is for life. The time relation shows the cooperation among the Arithmetic Coding. Next, Huffman code is to be presented scientific society and how the amended each other's work. The with simulation example and algorithm. The third is Lempel- paper presents the 12 pillars researched in this paper, and a Ziv-Welch (LZW) Algorithm which hatched more than 24 comparison table is to be developed. -
The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on Iot Nodes in Smart Cities
sensors Article The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities Ammar Nasif *, Zulaiha Ali Othman and Nor Samsiah Sani Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science & Technology, University Kebangsaan Malaysia, Bangi 43600, Malaysia; [email protected] (Z.A.O.); [email protected] (N.S.S.) * Correspondence: [email protected] Abstract: Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, Citation: Nasif, A.; Othman, Z.A.; we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Sani, N.S.