Comparative Study of Various Data Compression Techniques & an Iterative Algorithm to Compress Real Time Database
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A Robust Video Compression Algorithm for Efficient Data Transfer of Surveillance Data
Published by : International Journal of Engineering Research & Technology (IJERT) http://www.ijert.org ISSN: 2278-0181 Vol. 9 Issue 07, July-2020 A Robust Video Compression Algorithm for Efficient Data Transfer of Surveillance Data Kuldeep Kaur Baldeep Kaur Department of CSE Department of CSE LLRIET, Moga- 142001 LLRIET, Moga- 142001 Punjab- India Punjab- India Abstract - The compression models play an important role for Broadly compression can be classified into two the efficient bandwidth utilization for the streaming of the types: lossless compression and lossy compression. In this videos over the internet or TV channels. The proposed method paper a lossless compression technique is used to compress improves the problem of low compression ratio and the video. Lossless compression compresses the image by degradation of the image quality. The motive of present work encoding all the information from the original file, so when is to build a system in order to compress the video without reducing its quality and to reduce the complexity of existing the image is decompressed, it is exactly identical to the system. The present work is based upon the highly secure original image. Lossless compression technique involves no model for the purpose of video data compression in the lowest loss of information [8]. possible time. The methods used in this paper such as, Discrete Cosine Transform and Freeman Chain Code are used to II. LITERATURE REVIEW compress the data. The Embedded Zerotree Wavelet is used to The approach of two optimization procedures to improve improve the quality of the image whereas Discrete Wavelet the edge coding performance of Arithmetic edge coding Transform is used to achieve the higher compression ratio. -
Compression of Magnetic Resonance Imaging and Color Images Based on Decision Tree Encoding
International Journal of Pure and Applied Mathematics Volume 118 No. 24 2018 ISSN: 1314-3395 (on-line version) url: http://www.acadpubl.eu/hub/ Special Issue http://www.acadpubl.eu/hub/ Compression of magnetic resonance imaging and color images based on Decision tree encoding D. J. Ashpin Pabi,Dr. V. Arun Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Madanapalle, India May 24, 2018 Abstract Digital Images in the digital system are needed to com- press for an efficient storage with less memory space, archival purposes and also for the communication of information. Many compression techniques were developed in order to yield good reconstructed images after compression. An im- age compression algorithm for magnetic resonance imaging and color images based on the proposed decision tree encod- ing is created and implemented in this paper. Initially the given input image is divided into blocks and then each block are transformed using the discrete cosine transform (DCT) function. The thresholding quantization is applied to the generated DCT basis functions, reduces the redundancy on the image. The high frequency coefficients are then encoded using the proposed decisiontree encoding which is based on the run length encoding and the standard Huffman cod- ing. The implementation using such encoding techniques improves the performance of standard Huffman encoding in terms of assigning a lesser number of bits, which in turn reduces pixel length. The proposed decision tree encoding also increases the quality of the image at the decoder. Two data sets were used for testing the proposed decision tree 1 International Journal of Pure and Applied Mathematics Special Issue encoding scheme. -
Chain Code Compression Using String Transformation Techniques ∗ Borut Žalik, Domen Mongus, Krista Rizman Žalik, Niko Lukacˇ
Digital Signal Processing 53 (2016) 1–10 Contents lists available at ScienceDirect Digital Signal Processing www.elsevier.com/locate/dsp Chain code compression using string transformation techniques ∗ Borut Žalik, Domen Mongus, Krista Rizman Žalik, Niko Lukacˇ University of Maribor, Faculty of Electrical Engineering and Computer Science, Smetanova 17, SI-2000 Maribor, Slovenia a r t i c l e i n f o a b s t r a c t Article history: This paper considers the suitability of string transformation techniques for lossless chain codes’ Available online 17 March 2016 compression. The more popular chain codes are compressed including the Freeman chain code in four and eight directions, the vertex chain code, the three orthogonal chain code, and the normalised Keywords: directional chain code. A testing environment consisting of the constant 0-symbol Run-Length Encoding Chain codes (RLEL ), Move-To-Front Transformation (MTFT), and Burrows–Wheeler Transform (BWT) is proposed in Lossless compression 0 order to develop a more suitable configuration of these techniques for each type of the considered chain Burrows–Wheeler transform Move-to-front transform code. Finally, a simple yet efficient entropy coding is proposed consisting of MTFT, followed by the chain code symbols’ binarisation and the run-length encoding. PAQ8L compressor is also an option that can be considered in the final compression stage. Comparisons were done between the state-of-the-art including the Universal Chain Code Compression algorithm, Move-To-Front based algorithm, and an algorithm, based on the Markov model. Interesting conclusions were obtained from the experiments: the sequential L uses of MTFT, RLE0, and BWT are reasonable only in the cases of shorter chain codes’ alphabets as with the vertex chain code and the three orthogonal chain code. -
Nxadmin CLI Reference Guide Unity Iv Contents
HYPER-UNIFIED STORAGE nxadmin Command Line Interface Reference Guide NEXSAN | 325 E. Hillcrest Drive, Suite #150 | Thousand Oaks, CA 91360 USA Printed Thursday, July 26, 2018 | www.nexsan.com Copyright © 2010—2018 Nexsan Technologies, Inc. All rights reserved. Trademarks Nexsan® is a trademark or registered trademark of Nexsan Technologies, Inc. The Nexsan logo is a registered trademark of Nexsan Technologies, Inc. All other trademarks and registered trademarks are the property of their respective owners. Patents This product is protected by one or more of the following patents, and other pending patent applications worldwide: United States patents US8,191,841, US8,120,922; United Kingdom patents GB2466535B, GB2467622B, GB2467404B, GB2296798B, GB2297636B About this document Unauthorized use, duplication, or modification of this document in whole or in part without the written consent of Nexsan Corporation is strictly prohibited. Nexsan Technologies, Inc. reserves the right to make changes to this manual, as well as the equipment and software described in this manual, at any time without notice. This manual may contain links to web sites that were current at the time of publication, but have since been moved or become inactive. It may also contain links to sites owned and operated by third parties. Nexsan is not responsible for the content of any such third-party site. Contents Contents Contents iii Chapter 1: Accessing the nxadmin and nxcmd CLIs 15 Connecting to the Unity Storage System using SSH 15 Prerequisite 15 Connecting to the Unity -
16.1 Digital “Modes”
Contents 16.1 Digital “Modes” 16.5 Networking Modes 16.1.1 Symbols, Baud, Bits and Bandwidth 16.5.1 OSI Networking Model 16.1.2 Error Detection and Correction 16.5.2 Connected and Connectionless 16.1.3 Data Representations Protocols 16.1.4 Compression Techniques 16.5.3 The Terminal Node Controller (TNC) 16.1.5 Compression vs. Encryption 16.5.4 PACTOR-I 16.2 Unstructured Digital Modes 16.5.5 PACTOR-II 16.2.1 Radioteletype (RTTY) 16.5.6 PACTOR-III 16.2.2 PSK31 16.5.7 G-TOR 16.2.3 MFSK16 16.5.8 CLOVER-II 16.2.4 DominoEX 16.5.9 CLOVER-2000 16.2.5 THROB 16.5.10 WINMOR 16.2.6 MT63 16.5.11 Packet Radio 16.2.7 Olivia 16.5.12 APRS 16.3 Fuzzy Modes 16.5.13 Winlink 2000 16.3.1 Facsimile (fax) 16.5.14 D-STAR 16.3.2 Slow-Scan TV (SSTV) 16.5.15 P25 16.3.3 Hellschreiber, Feld-Hell or Hell 16.6 Digital Mode Table 16.4 Structured Digital Modes 16.7 Glossary 16.4.1 FSK441 16.8 References and Bibliography 16.4.2 JT6M 16.4.3 JT65 16.4.4 WSPR 16.4.5 HF Digital Voice 16.4.6 ALE Chapter 16 — CD-ROM Content Supplemental Files • Table of digital mode characteristics (section 16.6) • ASCII and ITA2 code tables • Varicode tables for PSK31, MFSK16 and DominoEX • Tips for using FreeDV HF digital voice software by Mel Whitten, KØPFX Chapter 16 Digital Modes There is a broad array of digital modes to service various needs with more coming. -
Smaller Flight Data Recorders{
Available online at http://docs.lib.purdue.edu/jate Journal of Aviation Technology and Engineering 2:2 (2013) 45–55 Smaller Flight Data Recorders{ Yair Wiseman and Alon Barkai Bar-Ilan University Abstract Data captured by flight data recorders are generally stored on the system’s embedded hard disk. A common problem is the lack of storage space on the disk. This lack of space for data storage leads to either a constant effort to reduce the space used by data, or to increased costs due to acquisition of additional space, which is not always possible. File compression can solve the problem, but carries with it the potential drawback of the increased overhead required when writing the data to the disk, putting an excessive load on the system and degrading system performance. The author suggests the use of an efficient compressed file system that both compresses data in real time and ensures that there will be minimal impact on the performance of other tasks. Keywords: flight data recorder, data compression, file system Introduction A flight data recorder is a small line-replaceable computer unit employed in aircraft. Its function is recording pilots’ inputs, electronic inputs, sensor positions and instructions sent to any electronic systems on the aircraft. It is unofficially referred to as a "black box". Flight data recorders are designed to be small and thoroughly fabricated to withstand the influence of high speed impacts and extreme temperatures. A flight data recorder from a commercial aircraft can be seen in Figure 1. State-of-the-art high density flash memory devices have permitted the solid state flight data recorder (SSFDR) to be implemented with much larger memory capacity. -
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. -
Digram Coding Based Lossless Data Compression Algorithm
Computing and Informatics, Vol. 29, 2010, 741–756 ISSDC: DIGRAM CODING BASED LOSSLESS DATA COMPRESSION ALGORITHM Altan Mesut, Aydin Carus Computer Engineering Department Trakya University Ahmet Karadeniz Yerleskesi Edirne, Turkey e-mail: {altanmesut, aydinc}@trakya.edu.tr Manuscript received 18 August 2008; revised 14 January 2009 Communicated by Jacek Kitowski Abstract. In this paper, a new lossless data compression method that is based on digram coding is introduced. This data compression method uses semi-static dic- tionaries: All of the used characters and most frequently used two character blocks (digrams) in the source are found and inserted into a dictionary in the first pass, compression is performed in the second pass. This two-pass structure is repeated several times and in every iteration particular number of elements is inserted in the dictionary until the dictionary is filled. This algorithm (ISSDC: Iterative Semi- Static Digram Coding) also includes some mechanisms that can decide about total number of iterations and dictionary size whenever these values are not given by the user. Our experiments show that ISSDC is better than LZW/GIF and BPE in com- pression ratio. It is worse than DEFLATE in compression of text and binary data, but better than PNG (which uses DEFLATE compression) in lossless compression of simple images. Keywords: Lossless data compression, dictionary-based compression, semi-static dictionary, digram coding 1 INTRODUCTION Lossy and lossless data compression techniques are widely used to increase capacity of data storage devices and to transfer data faster on networks. Lossy data com- 742 A. Mesut, A. Carus pression reduces the size of the source data by permanently eliminating redundant information. -
Important Processes Illustrated September 22, 2021
Important Processes Illustrated September 22, 2021 Copyright © 2012-2021 John Franklin Moore. All rights reserved. Contents Introduction ................................................................................................................................... 5 Consciousness>Sense .................................................................................................................... 6 space and senses ....................................................................................................................... 6 Consciousness>Sense>Hearing>Music ..................................................................................... 18 tone in music ........................................................................................................................... 18 Consciousness>Sense>Touch>Physiology ................................................................................ 23 haptic touch ............................................................................................................................ 23 Consciousness>Sense>Vision>Physiology>Depth Perception ................................................ 25 distance ratio .......................................................................................................................... 25 Consciousness>Sense>Vision>Physiology>Depth Perception ................................................ 31 triangulation by eye .............................................................................................................. -
The Illusion of Space and the Science of Data Compression And
The Illusion of Space and the Science of Data Compression and Deduplication Bruce Yellin EMC Proven Professional Knowledge Sharing 2010 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents What Was Old Is New Again ......................................................................................................... 4 The Business Benefits of Saving Space ....................................................................................... 6 Data Compression Strategies ....................................................................................................... 9 Data Compression Basics ....................................................................................................... 10 Compression Bakeoff .............................................................................................................. 13 Data Deduplication Strategies .................................................................................................... 16 Deduplication - Theory of Operation ....................................................................................... 16 File Level Deduplication - Single Instance Storage ................................................................. 21 Fixed-Block Deduplication ....................................................................................................... 23 Variable-Block Deduplication .................................................................................................. 24 Content-Aware Deduplication -
Data Compression in Solid State Storage
Data Compression in Solid State Storage John Fryar [email protected] Santa Clara, CA August 2013 1 Acknowledgements This presentation would not have been possible without the counsel, hard work and graciousness of the following individuals and/or organizations: Raymond Savarda Sandgate Technologies Santa Clara, CA August 2013 2 Disclaimers The opinions expressed herein are those of the author and do not necessarily represent those of any other organization or individual unless specifically cited. A thorough attempt to acknowledge all sources has been made. That said, we’re all human… Santa Clara, CA August 2013 3 Learning Objectives At the conclusion of this tutorial the audience will have been exposed to: • The different types of Data Compression • Common Data Compression Algorithms • The Deflate/Inflate (GZIP/GUNZIP) algorithms in detail • Implementation Options (Software/Hardware) • Impacts of design parameters in Performance • SSD benefits and challenges • Resources for Further Study Santa Clara, CA August 2013 4 Agenda • Background, Definitions, & Context • Data Compression Overview • Data Compression Algorithm Survey • Deflate/Inflate (GZIP/GUNZIP) in depth • Software Implementations • HW Implementations • Tradeoffs & Advanced Topics • SSD Benefits and Challenges • Conclusions Santa Clara, CA August 2013 5 Definitions Item Description Comments Open A system which will compress Must strictly adhere to standards System data for use by other entities. on compress / decompress I.E. the compressed data will algorithms exit the system Interoperability among vendors mandated for Open Systems Closed A system which utilizes Can support a limited, optimized System compressed data internally but subset of standard. does not expose compressed Also allows custom algorithms data to the outside world No Interoperability req’d.