The Basic Principles of Data Compression
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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. -
Melinda's Marks Merit Main Mantle SYDNEY STRIDERS
SYDNEY STRIDERS ROAD RUNNERS’ CLUB AUSTRALIA EDITION No 108 MAY - AUGUST 2009 Melinda’s marks merit main mantle This is proving a “best-so- she attained through far” year for Melinda. To swimming conflicted with date she has the fastest her transition to running. time in Australia over 3000m. With a smart 2nd Like all top runners she at the State Open 5000m does well over 100k a champs, followed by a week in training, win at the State Open 10k consisting of a variety of Road Champs, another sessions: steady pace, win at the Herald Half medium pace, long slow which doubles as the runs, track work, fartlek, State Half Champs and a hills, gym work and win at the State Cross swimming! country Champs, our Melinda is looking like Springs under her shoes give hot property. Melinda extra lift Melinda began her sports Continued Page 3 career as a swimmer. By 9 years of age she was representing her club at State level. She held numerous records for INSIDE BLISTER 108 Breaststroke and Lisa facing racing pacing Butterfly. Her switch to running came after the McKinney makes most of death of her favourite marvellous mud moment Coach and because she Weather woe means Mo wasn’t growing as big as can’t crow though not slow! her fellow competitors. She managed some pretty fast times at inter-schools Brent takes tumble at Trevi champs and Cross Country before making an impression in the Open category where she has Champion Charles cheered steadily improved. by chance & chase challenge N’Lotsa Uthastuff Melinda credits her swimming background for endurance -
End-To-End Enterprise Encryption: a Look at Securezip® Technology
End-to-End Enterprise Encryption: A Look at SecureZIP® Technology TECHNICAL WHITE PAPER WP 700.xxxx End-to-End Enterprise Encryption: A Look at SecureZIP Technology Table of Contents SecureZIP Executive Summary 3 SecureZIP: The Next Generation of ZIP 4 PKZIP: The Foundation for SecureZIP 4 Implementation of ZIP Encryption 5 Hybrid Cryptosystem 6 Crytopgraphic Calculation Sources 7 Digital Signing 7 In Step with the Data Protection Market’s Needs 7 Conclusion 8 WP-SZ-032609 | 2 End-to-End Enterprise Encryption: A Look at SecureZIP Technology End-to-End Enterprise Encryption: A Look at SecureZIP Technology Every day sensitive data is exchanged within your organization, both internally and with external partners. Personal health & insurance data of your employees is shared between your HR department and outside insurance carriers. Customer PII (Personally Identifiable Information) is transferred from your corporate headquarters to various offices around the world. Payment transaction data flows between your store locations and your payments processor. All of these instances involve sensitive data and regulated information that must be exchanged between systems, locations, and partners; a breach of any of them could lead to irreparable damage to your reputation and revenue. Organizations today must adopt a means for mitigating the internal and external risks of data breach and compromise. The required solution must support the exchange of data across operating systems to account for both the diversity of your own infrastructure and the unknown infrastructures of your customers, partners, and vendors. Moreover, that solution must integrate naturally into your existing workflows to keep operational cost and impact to minimum while still protecting data end-to-end. -
Word-Based Text Compression
WORD-BASED TEXT COMPRESSION Jan Platoš, Jiří Dvorský Department of Computer Science VŠB – Technical University of Ostrava, Czech Republic {jan.platos.fei, jiri.dvorsky}@vsb.cz ABSTRACT Today there are many universal compression algorithms, but in most cases is for specific data better using specific algorithm - JPEG for images, MPEG for movies, etc. For textual documents there are special methods based on PPM algorithm or methods with non-character access, e.g. word-based compression. In the past, several papers describing variants of word- based compression using Huffman encoding or LZW method were published. The subject of this paper is the description of a word-based compression variant based on the LZ77 algorithm. The LZ77 algorithm and its modifications are described in this paper. Moreover, various ways of sliding window implementation and various possibilities of output encoding are described, as well. This paper also includes the implementation of an experimental application, testing of its efficiency and finding the best combination of all parts of the LZ77 coder. This is done to achieve the best compression ratio. In conclusion there is comparison of this implemented application with other word-based compression programs and with other commonly used compression programs. Key Words: LZ77, word-based compression, text compression 1. Introduction Data compression is used more and more the text compression. In the past, word- in these days, because larger amount of based compression methods based on data require to be transferred or backed-up Huffman encoding, LZW or BWT were and capacity of media or speed of network tested. This paper describes word-based lines increase slowly. -
Lzw Compression and Decompression
LZW COMPRESSION AND DECOMPRESSION December 4, 2015 1 Contents 1 INTRODUCTION 3 2 CONCEPT 3 3 COMPRESSION 3 4 DECOMPRESSION: 4 5 ADVANTAGES OF LZW: 6 6 DISADVANTAGES OF LZW: 6 2 1 INTRODUCTION LZW stands for Lempel-Ziv-Welch. This algorithm was created in 1984 by these people namely Abraham Lempel, Jacob Ziv, and Terry Welch. This algorithm is very simple to implement. In 1977, Lempel and Ziv published a paper on the \sliding-window" compression followed by the \dictionary" based compression which were named LZ77 and LZ78, respectively. later, Welch made a contri- bution to LZ78 algorithm, which was then renamed to be LZW Compression algorithm. 2 CONCEPT Many files in real time, especially text files, have certain set of strings that repeat very often, for example " The ","of","on"etc., . With the spaces, any string takes 5 bytes, or 40 bits to encode. But what if we need to add the whole string to the list of characters after the last one, at 256. Then every time we came across the string like" the ", we could send the code 256 instead of 32,116,104 etc.,. This would take 9 bits instead of 40bits. This is the algorithm of LZW compression. It starts with a "dictionary" of all the single character with indexes from 0 to 255. It then starts to expand the dictionary as information gets sent through. Pretty soon, all the strings will be encoded as a single bit, and compression would have occurred. LZW compression replaces strings of characters with single codes. It does not analyze the input text. -
Encryption Introduction to Using 7-Zip
IT Services Training Guide Encryption Introduction to using 7-Zip It Services Training Team The University of Manchester email: [email protected] www.itservices.manchester.ac.uk/trainingcourses/coursesforstaff Version: 5.3 Training Guide Introduction to Using 7-Zip Page 2 IT Services Training Introduction to Using 7-Zip Table of Contents Contents Introduction ......................................................................................................................... 4 Compress/encrypt individual files ....................................................................................... 5 Email compressed/encrypted files ....................................................................................... 8 Decrypt an encrypted file ..................................................................................................... 9 Create a self-extracting encrypted file .............................................................................. 10 Decrypt/un-zip a file .......................................................................................................... 14 APPENDIX A Downloading and installing 7-Zip ................................................................. 15 Help and Further Reference ............................................................................................... 18 Page 3 Training Guide Introduction to Using 7-Zip Introduction 7-Zip is an application that allows you to: Compress a file – for example a file that is 5MB can be compressed to 3MB Secure the -
Steganography and Vulnerabilities in Popular Archives Formats.| Nyxengine Nyx.Reversinglabs.Com
Hiding in the Familiar: Steganography and Vulnerabilities in Popular Archives Formats.| NyxEngine nyx.reversinglabs.com Contents Introduction to NyxEngine ............................................................................................................................ 3 Introduction to ZIP file format ...................................................................................................................... 4 Introduction to steganography in ZIP archives ............................................................................................. 5 Steganography and file malformation security impacts ............................................................................... 8 References and tools .................................................................................................................................... 9 2 Introduction to NyxEngine Steganography1 is the art and science of writing hidden messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message, a form of security through obscurity. When it comes to digital steganography no stone should be left unturned in the search for viable hidden data. Although digital steganography is commonly used to hide data inside multimedia files, a similar approach can be used to hide data in archives as well. Steganography imposes the following data hiding rule: Data must be hidden in such a fashion that the user has no clue about the hidden message or file's existence. This can be achieved by -
Randomized Lempel-Ziv Compression for Anti-Compression Side-Channel Attacks
Randomized Lempel-Ziv Compression for Anti-Compression Side-Channel Attacks by Meng Yang A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Applied Science in Electrical and Computer Engineering Waterloo, Ontario, Canada, 2018 c Meng Yang 2018 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Security experts confront new attacks on TLS/SSL every year. Ever since the compres- sion side-channel attacks CRIME and BREACH were presented during security conferences in 2012 and 2013, online users connecting to HTTP servers that run TLS version 1.2 are susceptible of being impersonated. We set up three Randomized Lempel-Ziv Models, which are built on Lempel-Ziv77, to confront this attack. Our three models change the determin- istic characteristic of the compression algorithm: each compression with the same input gives output of different lengths. We implemented SSL/TLS protocol and the Lempel- Ziv77 compression algorithm, and used them as a base for our simulations of compression side-channel attack. After performing the simulations, all three models successfully pre- vented the attack. However, we demonstrate that our randomized models can still be broken by a stronger version of compression side-channel attack that we created. But this latter attack has a greater time complexity and is easily detectable. Finally, from the results, we conclude that our models couldn't compress as well as Lempel-Ziv77, but they can be used against compression side-channel attacks. -
Optimal Parsing for Dictionary Text Compression Alessio Langiu
Optimal Parsing for dictionary text compression Alessio Langiu To cite this version: Alessio Langiu. Optimal Parsing for dictionary text compression. Other [cs.OH]. Université Paris-Est, 2012. English. NNT : 2012PEST1091. tel-00804215 HAL Id: tel-00804215 https://tel.archives-ouvertes.fr/tel-00804215 Submitted on 25 Mar 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. UniversitadegliStudidiPalermo` Dipartimento di Matematica e Applicazioni Dottorato di Ricerca in Matematica e Informatica XXIII◦ Ciclo - S.S.D. Inf/01 UniversiteParis-Est´ Ecole´ doctorale MSTIC Th´ese de doctorat en Informatique Optimal Parsing for Dictionary Text Compression Author: LANGIU Alessio Ph.D. Thesis in Computer Science April 3, 2012 PhD Commission: Thesis Directors: Prof. CROCHEMORE Maxime University of Paris-Est Prof. RESTIVO Antonio University of Palermo Examiners: Prof. ILIOPOULOS Costas King’s College London Prof. LECROQ Thierry University of Rouen Prof. MIGNOSI Filippo University of L’Aquila Referees: Prof. GROSSI Roberto University of Pisa Prof. ILIOPOULOS Costas King’s College London Prof. LECROQ Thierry University of Rouen ii Summary Dictionary-based compression algorithms include a parsing strategy to transform the input text into a sequence of dictionary phrases. Given a text, such process usually is not unique and, for compression purposes, it makes sense to find one of the possible parsing that minimize the final compres- sion ratio. -
Dictionary Based Compression for Images
INTERNATIONAL JOURNAL OF COMPUTERS Issue 3, Volume 6, 2012 Dictionary Based Compression for Images Bruno Carpentieri Ziv and Lempel in [1]. Abstract—Lempel-Ziv methods were original introduced to By limiting what could enter the dictionary, LZ2 assures compress one-dimensional data (text, object codes, etc.) but recently that there is at most one instance for each possible pattern in they have been successfully used in image compression. the dictionary. Constantinescu and Storer in [6] introduced a single-pass vector Initially the dictionary is empty. The coding pass consists of quantization algorithm that, with no training or previous knowledge of the digital data was able to achieve better compression results with searching the dictionary for the longest entry that is a prefix of respect to the JPEG standard and had also important computational a string starting at the current coding position. advantages. The index of the match is transmitted to the decoder using We review some of our recent work on LZ-based, single pass, log N bits, where N is the current size of the dictionary. adaptive algorithms for the compression of digital images, taking into 2 account the theoretical optimality of these approach, and we A new pattern is introduced into the dictionary by experimentally analyze the behavior of this algorithm with respect to concatenating the current match with the next character that the local dictionary size and with respect to the compression of bi- has to be encoded. level images. The dictionary of LZ2 continues to grow throughout the coding process. Keywords—Image compression, textual substitution methods. -
A Software Tool for Data Compression Using the LZ77 ("Sliding Window") Algorithm Student Authors: Vladan R
A Software Tool for Data Compression Using the LZ77 ("Sliding Window") Algorithm Student authors: Vladan R. Djokić1, Miodrag G. Vidojković1 Mentors: Radomir S. Stanković2, Dušan B. Gajić2 Abstract – Data compression is a field of computer science that close the paper with some conclusions in the final section. is always in need of fast algorithms and their efficient implementations. Lempel-Ziv algorithm is the first which used a dictionary method for data compression. In this paper, we II. LEMPEL-ZIV ALGORITHM present the software implementation of this so-called "sliding window" method for compression and decompression. We also include experimental results considering data compression rate A. Theoretical basis and running time. This software tool includes a graphical user interface and is meant for use in educational purposes. The Lempel-Ziv algorithm [1] is an algorithm for lossless data compression. It is actually a whole family of algorithms, Keywords – Lempel-Ziv algorithm, C# programming solution, (see Figure 1) stemming from the two original algorithms that text compression. were first proposed by Jacob Ziv and Abraham Lempel in their landmark papers in 1977. [1] and 1978. [2]. LZ77 and I. INTRODUCTION LZ78 got their name by year of publishing. The Lempel-Ziv algorithms belong to adaptive dictionary While reading a book it is noticeable that some words are coders [1]. On start of encoding process, dictionary does not repeating very often. In computer world, textual files exist. The dictionary is created during encoding. There is no represent those books and same logic may be applied there. final state of dictionary and it does not have fixed size.