The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on Iot Nodes in Smart Cities
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Compressed Transitive Delta Encoding 1. Introduction
Compressed Transitive Delta Encoding Dana Shapira Department of Computer Science Ashkelon Academic College Ashkelon 78211, Israel [email protected] Abstract Given a source file S and two differencing files ∆(S; T ) and ∆(T;R), where ∆(X; Y ) is used to denote the delta file of the target file Y with respect to the source file X, the objective is to be able to construct R. This is intended for the scenario of upgrading soft- ware where intermediate releases are missing, or for the case of file system backups, where non consecutive versions must be recovered. The traditional way is to decompress ∆(S; T ) in order to construct T and then apply ∆(T;R) on T and obtain R. The Compressed Transitive Delta Encoding (CTDE) paradigm, introduced in this paper, is to construct a delta file ∆(S; R) working directly on the two given delta files, ∆(S; T ) and ∆(T;R), without any decompression or the use of the base file S. A new algorithm for solving CTDE is proposed and its compression performance is compared against the traditional \double delta decompression". Not only does it use constant additional space, as opposed to the traditional method which uses linear additional memory storage, but experiments show that the size of the delta files involved is reduced by 15% on average. 1. Introduction Differential file compression represents a target file T with respect to a source file S. That is, both the encoder and decoder have available identical copies of S. A new file T is encoded and subsequently decoded by making use of S. -
Arithmetic Coding
Arithmetic Coding Arithmetic coding is the most efficient method to code symbols according to the probability of their occurrence. The average code length corresponds exactly to the possible minimum given by information theory. Deviations which are caused by the bit-resolution of binary code trees do not exist. In contrast to a binary Huffman code tree the arithmetic coding offers a clearly better compression rate. Its implementation is more complex on the other hand. In arithmetic coding, a message is encoded as a real number in an interval from one to zero. Arithmetic coding typically has a better compression ratio than Huffman coding, as it produces a single symbol rather than several separate codewords. Arithmetic coding differs from other forms of entropy encoding such as Huffman coding in that rather than separating the input into component symbols and replacing each with a code, arithmetic coding encodes the entire message into a single number, a fraction n where (0.0 ≤ n < 1.0) Arithmetic coding is a lossless coding technique. There are a few disadvantages of arithmetic coding. One is that the whole codeword must be received to start decoding the symbols, and if there is a corrupt bit in the codeword, the entire message could become corrupt. Another is that there is a limit to the precision of the number which can be encoded, thus limiting the number of symbols to encode within a codeword. There also exist many patents upon arithmetic coding, so the use of some of the algorithms also call upon royalty fees. Arithmetic coding is part of the JPEG data format. -
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. -
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. -
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE LOSSLESS COMPRESSION of SATELLITE TELEMETRY DATA for a NARROW-BAND DOWNLINK a Graduate P
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE LOSSLESS COMPRESSION OF SATELLITE TELEMETRY DATA FOR A NARROW-BAND DOWNLINK A graduate project submitted in partial fulfillment of the requirements For the degree of Master of Science in Electrical Engineering By Gor Beglaryan May 2014 Copyright Copyright (c) 2014, Gor Beglaryan Permission to use, copy, modify, and/or distribute the software developed for this project for any purpose with or without fee is hereby granted. THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. Copyright by Gor Beglaryan ii Signature Page The graduate project of Gor Beglaryan is approved: __________________________________________ __________________ Prof. James A Flynn Date __________________________________________ __________________ Dr. Deborah K Van Alphen Date __________________________________________ __________________ Dr. Sharlene Katz, Chair Date California State University, Northridge iii Contents Copyright .......................................................................................................................................... ii Signature Page ............................................................................................................................... -
A Novel Coding Architecture for Multi-Line Lidar Point Clouds
This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 1 A Novel Coding Architecture for Multi-Line LiDAR Point Clouds Based on Clustering and Convolutional LSTM Network Xuebin Sun , Sukai Wang , Graduate Student Member, IEEE, and Ming Liu , Senior Member, IEEE Abstract— Light detection and ranging (LiDAR) plays an preservation of historical relics, 3D sensing for smart city, indispensable role in autonomous driving technologies, such as well as autonomous driving. Especially for autonomous as localization, map building, navigation and object avoidance. driving systems, LiDAR sensors play an indispensable role However, due to the vast amount of data, transmission and storage could become an important bottleneck. In this article, in a large number of key techniques, such as simultaneous we propose a novel compression architecture for multi-line localization and mapping (SLAM) [1], path planning [2], LiDAR point cloud sequences based on clustering and convolu- obstacle avoidance [3], and navigation. A point cloud consists tional long short-term memory (LSTM) networks. LiDAR point of a set of individual 3D points, in accordance with one or clouds are structured, which provides an opportunity to convert more attributes (color, reflectance, surface normal, etc). For the 3D data to 2D array, represented as range images. Thus, we cast the 3D point clouds compression as a range image instance, the Velodyne HDL-64E LiDAR sensor generates a sequence compression problem. Inspired by the high efficiency point cloud of up to 2.2 billion points per second, with a video coding (HEVC) algorithm, we design a novel compression range of up to 120 m. -
Incompressibility and Lossless Data Compression: an Approach By
Incompressibility and Lossless Data Compression: An Approach by Pattern Discovery Incompresibilidad y compresión de datos sin pérdidas: Un acercamiento con descubrimiento de patrones Oscar Herrera Alcántara and Francisco Javier Zaragoza Martínez Universidad Autónoma Metropolitana Unidad Azcapotzalco Departamento de Sistemas Av. San Pablo No. 180, Col. Reynosa Tamaulipas Del. Azcapotzalco, 02200, Mexico City, Mexico Tel. 53 18 95 32, Fax 53 94 45 34 [email protected], [email protected] Article received on July 14, 2008; accepted on April 03, 2009 Abstract We present a novel method for lossless data compression that aims to get a different performance to those proposed in the last decades to tackle the underlying volume of data of the Information and Multimedia Ages. These latter methods are called entropic or classic because they are based on the Classic Information Theory of Claude E. Shannon and include Huffman [8], Arithmetic [14], Lempel-Ziv [15], Burrows Wheeler (BWT) [4], Move To Front (MTF) [3] and Prediction by Partial Matching (PPM) [5] techniques. We review the Incompressibility Theorem and its relation with classic methods and our method based on discovering symbol patterns called metasymbols. Experimental results allow us to propose metasymbolic compression as a tool for multimedia compression, sequence analysis and unsupervised clustering. Keywords: Incompressibility, Data Compression, Information Theory, Pattern Discovery, Clustering. Resumen Presentamos un método novedoso para compresión de datos sin pérdidas que tiene por objetivo principal lograr un desempeño distinto a los propuestos en las últimas décadas para tratar con los volúmenes de datos propios de la Era de la Información y la Era Multimedia. -
Modification of Adaptive Huffman Coding for Use in Encoding Large Alphabets
ITM Web of Conferences 15, 01004 (2017) DOI: 10.1051/itmconf/20171501004 CMES’17 Modification of Adaptive Huffman Coding for use in encoding large alphabets Mikhail Tokovarov1,* 1Lublin University of Technology, Electrical Engineering and Computer Science Faculty, Institute of Computer Science, Nadbystrzycka 36B, 20-618 Lublin, Poland Abstract. The paper presents the modification of Adaptive Huffman Coding method – lossless data compression technique used in data transmission. The modification was related to the process of adding a new character to the coding tree, namely, the author proposes to introduce two special nodes instead of single NYT (not yet transmitted) node as in the classic method. One of the nodes is responsible for indicating the place in the tree a new node is attached to. The other node is used for sending the signal indicating the appearance of a character which is not presented in the tree. The modified method was compared with existing methods of coding in terms of overall data compression ratio and performance. The proposed method may be used for large alphabets i.e. for encoding the whole words instead of separate characters, when new elements are added to the tree comparatively frequently. Huffman coding is frequently chosen for implementing open source projects [3]. The present paper contains the 1 Introduction description of the modification that may help to improve Efficiency and speed – the two issues that the current the algorithm of adaptive Huffman coding in terms of world of technology is centred at. Information data savings. technology (IT) is no exception in this matter. Such an area of IT as social media has become extremely popular 2 Study of related works and widely used, so that high transmission speed has gained a great importance. -
Greedy Algorithm Implementation in Huffman Coding Theory
iJournals: International Journal of Software & Hardware Research in Engineering (IJSHRE) ISSN-2347-4890 Volume 8 Issue 9 September 2020 Greedy Algorithm Implementation in Huffman Coding Theory Author: Sunmin Lee Affiliation: Seoul International School E-mail: [email protected] <DOI:10.26821/IJSHRE.8.9.2020.8905 > ABSTRACT In the late 1900s and early 2000s, creating the code All aspects of modern society depend heavily on data itself was a major challenge. However, now that the collection and transmission. As society grows more basic platform has been established, efficiency that can dependent on data, the ability to store and transmit it be achieved through data compression has become the efficiently has become more important than ever most valuable quality current technology deeply before. The Huffman coding theory has been one of desires to attain. Data compression, which is used to the best coding methods for data compression without efficiently store, transmit and process big data such as loss of information. It relies heavily on a technique satellite imagery, medical data, wireless telephony and called a greedy algorithm, a process that “greedily” database design, is a method of encoding any tries to find an optimal solution global solution by information (image, text, video etc.) into a format that solving for each optimal local choice for each step of a consumes fewer bits than the original data. [8] Data problem. Although there is a disadvantage that it fails compression can be of either of the two types i.e. lossy to consider the problem as a whole, it is definitely or lossless compressions. -
Information Theory Revision (Source)
ELEC3203 Digital Coding and Transmission – Overview & Information Theory S Chen Information Theory Revision (Source) {S(k)} {b i } • Digital source is defined by digital source source coding 1. Symbol set: S = {mi, 1 ≤ i ≤ q} symbols/s bits/s 2. Probability of occurring of mi: pi, 1 ≤ i ≤ q 3. Symbol rate: Rs [symbols/s] 4. Interdependency of {S(k)} • Information content of alphabet mi: I(mi) = − log2(pi) [bits] • Entropy: quantifies average information conveyed per symbol q – Memoryless sources: H = − pi · log2(pi) [bits/symbol] i=1 – 1st-order memory (1st-order Markov)P sources with transition probabilities pij q q q H = piHi = − pi pij · log2(pij) [bits/symbol] Xi=1 Xi=1 Xj=1 • Information rate: tells you how many bits/s information the source really needs to send out – Information rate R = Rs · H [bits/s] • Efficient source coding: get rate Rb as close as possible to information rate R – Memoryless source: apply entropy coding, such as Shannon-Fano and Huffman, and RLC if source is binary with most zeros – Generic sources with memory: remove redundancy first, then apply entropy coding to “residauls” 86 ELEC3203 Digital Coding and Transmission – Overview & Information Theory S Chen Practical Source Coding • Practical source coding is guided by information theory, with practical constraints, such as performance and processing complexity/delay trade off • When you come to practical source coding part, you can smile – as you should know everything • As we will learn, data rate is directly linked to required bandwidth, source coding is to encode source with a data rate as small as possible, i.e.