Compressing Integers for Fast File Access
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Error Correction Capacity of Unary Coding
Error Correction Capacity of Unary Coding Pushpa Sree Potluri1 Abstract Unary coding has found applications in data compression, neural network training, and in explaining the production mechanism of birdsong. Unary coding is redundant; therefore it should have inherent error correction capacity. An expression for the error correction capability of unary coding for the correction of single errors has been derived in this paper. 1. Introduction The unary number system is the base-1 system. It is the simplest number system to represent natural numbers. The unary code of a number n is represented by n ones followed by a zero or by n zero bits followed by 1 bit [1]. Unary codes have found applications in data compression [2],[3], neural network training [4]-[11], and biology in the study of avian birdsong production [12]-14]. One can also claim that the additivity of physics is somewhat like the tallying of unary coding [15],[16]. Unary coding has also been seen as the precursor to the development of number systems [17]. Some representations of unary number system use n-1 ones followed by a zero or with the corresponding number of zeroes followed by a one. Here we use the mapping of the left column of Table 1. Table 1. An example of the unary code N Unary code Alternative code 0 0 0 1 10 01 2 110 001 3 1110 0001 4 11110 00001 5 111110 000001 6 1111110 0000001 7 11111110 00000001 8 111111110 000000001 9 1111111110 0000000001 10 11111111110 00000000001 The unary number system may also be seen as a space coding of numerical information where the location determines the value of the number. -
Image Data Compression Introduction to Coding
Image Data Compression Introduction to Coding © 2018-19 Alexey Pak, Lehrstuhl für Interaktive Echtzeitsysteme, Fakultät für Informatik, KIT 1 Review: data reduction steps (discretization / digitization) Continuous 2D siGnal Fully diGital siGnal (liGht intensity on sensor) gq (xa, yb,ti ) Discrete time siGnal (pixel voltaGe readinGs) g(xa, yb,ti ) g(x, y,t) Spatial discretization Temporal discretization and diGitization g(xa, yb,t) g(xa, yb,t) gq (xa, yb,t) Discrete value siGnal AnaloG siGnal Spatially discrete siGnal (e.G., # of electrons at each (liGht intensity at a pixel) (pixel-averaGed intensity) pixel of the CCD matrix) © 2018-19 Alexey Pak, Lehrstuhl für Interaktive Echtzeitsysteme, Fakultät für Informatik, KIT 2 Review: data reduction steps (discretization / digitization) Discretization of 1D continuous-time signals (sampling) • Important signal transformations: up- and down-sampling • Information-preserving down-sampling: rate determined based on signal bandwidth • Fourier space allows simple interpretation of the effects due to decimation and interpolation (techniques of up-/down-sampling) Scalar (one-dimensional) signal quantization of continuous-value signals • Quantizer types: uniform, simple non-uniform (with a dead zone, with a limited amplitude) • Advanced quantizers: PDF-optimized (Max-Lloyd algorithm), perception-optimized, SNR- optimized • Implementation: pre-processing with a compander function + simple quantization Vector (multi-dimensional) signal quantization • Terminology: quantization, reconstruction, codebook, distance metric, Voronoi regions, space partitioning • Relation to the general classification problem (from Machine Learning) • Linde-Buzo-Gray algorithm of constructing (sub-optimal) codebooks (aka k-means) © 2018-19 Alexey Pak, Lehrstuhl für Interaktive Echtzeitsysteme, Fakultät für Informatik, KIT 3 LGB vector quantization – 2D example [Linde, Buzo, Gray ‘80]: 1. -
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. -
Pixel-Based Video Coding
UPTEC IT 14 003 Examensarbete 30 hp Mars 2014 Pixel-based video coding Johannes Olsson Sandgren Abstract Pixel-based video coding Johannes Olsson Sandgren Teknisk- naturvetenskaplig fakultet UTH-enheten This paper studies the possibilities of extending the pixel-based compression algorithm LOCO-I, used by the lossless and near lossless image compression standard Besöksadress: JPEG-LS, introduced by the Joint Photographic Experts Group (JPEG) in 1999, to Ångströmlaboratoriet Lägerhyddsvägen 1 video sequences and very low bit-rates. Bitrates below 1 bit per pixel are achieved Hus 4, Plan 0 through skipping signaling when the prediction of a pixels sufficiently good. The pixels to be skipped are implicitly detected by the decoder, minimizing the overhead. Postadress: Different methods of quantization are tested, and the possibility of using vector Box 536 751 21 Uppsala quantization is investigated, by matching pixel sequences against a dynamically generated vector tree. Several different prediction schemes are evaluated, both linear Telefon: and non-linear, with both static and adaptive weights. Maintaining the low 018 – 471 30 03 computational complexity of LOCO-I has been a priority. The results are compared Telefax: to different HEVC implementations with regards to compression speed and ratio. 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student Handledare: Jonatan Samuelsson Ämnesgranskare: Cris Luengo Examinator: Lars-Åke Nordén ISSN: 1401-5749, UPTEC IT 14 003 Tryckt av: Reprocentralen ITC Sammanfattning på svenska De dominerande videokomprimeringsstandarderna idag, som H.26x, är blockbaserade, och har förhållandevis hög beräkningsmässig komplexitet, framförallt i kodningsfasen. I följande text utforskas möjligheten att utöka en välkänd algoritm, LOCO-I, för pixel- baserad komprimering så att komprimering lägre än 1 bit per pixel blir möjlig. -
Habilitation `A Diriger Des Recherches from Image Coding And
Habilitation `aDiriger des Recherches From image coding and representation to robotic vision Marie BABEL Universit´ede Rennes 1 June 29th 2012 Bruno Arnaldi, Professor, INSA Rennes Committee chairman Ferran Marques, Professor, Technical University of Catalonia Reviewer Beno^ıtMacq, Professor, Universit´eCatholique de Louvain Reviewer Fr´ed´ericDufaux, CNRS Research Director, Telecom ParisTech Reviewer Charly Poulliat, Professor, INP-ENSEEIHT Toulouse Examiner Claude Labit, Inria Research Director, Inria Rennes Examiner Fran¸oisChaumette, Inria Research Director, Inria Rennes Examiner Joseph Ronsin, Professor, INSA Rennes Examiner IRISA UMR CNRS 6074 / INRIA - Equipe Lagadic IETR UMR CNRS 6164 - Equipe Images 2 Contents 1 Introduction 3 1.1 An overview of my research project ........................... 3 1.2 Coding and representation tools: QoS/QoE context .................. 4 1.3 Image and video representation: towards pseudo-semantic technologies . 4 1.4 Organization of the document ............................. 5 2 Still image coding and advanced services 7 2.1 JPEG AIC calls for proposal: a constrained applicative context ............ 8 2.1.1 Evolution of codecs: JPEG committee ..................... 8 2.1.2 Response to the call for JPEG-AIC ....................... 9 2.2 Locally Adaptive Resolution compression framework: an overview . 10 2.2.1 Principles and properties ............................ 11 2.2.2 Lossy to lossless scalable solution ........................ 12 2.2.3 Hierarchical colour region representation and coding . 12 2.2.4 Interoperability ................................. 13 2.3 Quadtree Partitioning: principles ............................ 14 2.3.1 Basic homogeneity criterion: morphological gradient . 14 2.3.2 Enhanced color-oriented homogeneity criterion . 15 2.3.2.1 Motivations .............................. 15 2.3.2.2 Results ................................ 16 2.4 Interleaved S+P: the pyramidal profile ........................ -
Efficient Inverted Index Compression Algorithm Characterized by Faster
entropy Article Efficient Inverted Index Compression Algorithm Characterized by Faster Decompression Compared with the Golomb-Rice Algorithm Andrzej Chmielowiec 1,* and Paweł Litwin 2 1 The Faculty of Mechanics and Technology, Rzeszow University of Technology, Kwiatkowskiego 4, 37-450 Stalowa Wola, Poland 2 The Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, Powsta´ncówWarszawy 8, 35-959 Rzeszow, Poland; [email protected] * Correspondence: [email protected] Abstract: This article deals with compression of binary sequences with a given number of ones, which can also be considered as a list of indexes of a given length. The first part of the article shows that the entropy H of random n-element binary sequences with exactly k elements equal one satisfies the inequalities k log2(0.48 · n/k) < H < k log2(2.72 · n/k). Based on this result, we propose a simple coding using fixed length words. Its main application is the compression of random binary sequences with a large disproportion between the number of zeros and the number of ones. Importantly, the proposed solution allows for a much faster decompression compared with the Golomb-Rice coding with a relatively small decrease in the efficiency of compression. The proposed algorithm can be particularly useful for database applications for which the speed of decompression is much more important than the degree of index list compression. Citation: Chmielowiec, A.; Litwin, P. Efficient Inverted Index Compression Keywords: inverted index compression; Golomb-Rice coding; runs coding; sparse binary sequence Algorithm Characterized by Faster compression Decompression Compared with the Golomb-Rice Algorithm. -
Techniques for Inverted Index Compression
Techniques for Inverted Index Compression GIULIO ERMANNO PIBIRI, ISTI-CNR, Italy ROSSANO VENTURINI, University of Pisa, Italy The data structure at the core of large-scale search engines is the inverted index, which is essentially a collection of sorted integer sequences called inverted lists. Because of the many documents indexed by such engines and stringent performance requirements imposed by the heavy load of queries, the inverted index stores billions of integers that must be searched efficiently. In this scenario, index compression is essential because it leads to a better exploitation of the computer memory hierarchy for faster query processing and, at the same time, allows reducing the number of storage machines. The aim of this article is twofold: first, surveying the encoding algorithms suitable for inverted index compression and, second, characterizing the performance of the inverted index through experimentation. CCS Concepts: • Information systems → Search index compression; Search engine indexing. Additional Key Words and Phrases: Inverted Indexes; Data Compression; Efficiency ACM Reference Format: Giulio Ermanno Pibiri and Rossano Venturini. 2020. Techniques for Inverted Index Compression. ACM Comput. Surv. 1, 1 (August 2020), 35 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn 1 INTRODUCTION Consider a collection of textual documents each described, for this purpose, as a set of terms. For each distinct term t appearing in the collection, an integer sequence St is built and lists, in sorted order, all the identifiers of the documents (henceforth, docIDs) where the term appears. The sequence St is called the inverted list, or posting list, of the term t and the set of inverted lists for all the distinct terms is the subject of this article – the data structure known as the inverted index. -
Vosoughi, Mbw2, Cavallar}@Rice.Edu
Baseband Signal Compression in Wireless Base Stations Aida Vosoughi, Michael Wu, and Joseph R. Cavallaro {vosoughi, mbw2, cavallar}@rice.edu Unused Significant USBR code Motivation 010, 5(=101) Experimental Setup and Results Bit Removal binary code 01011001 11001 Using WARP hardware and WARPLab framework for over-the- With new wireless standards, base stations require 01010000 10000 Used before in air experiments: (a) Downlink, (b) Uplink greater data rates over the serial data link between base 01000111 00111 Network on chip serial station processor and (remote) RF unit. 01001100 01100 link compression 10011100 1001, 4(=100) This link is especially important in: 10010011 1100 10011010 0011 • Distributed Antenna Systems 10010001 1010 • Cooperating base stations …. 0001 • Cloud-based Radio Access Networks … We propose to use a differencing step before compressing with Baseband signals: each of the above lossless schemes. • Usually oversampled and correlated Compression Ratio Results: • Represented with extra bit resolution Proposed Lossy Compression Lossless Schemes: Lossy Scheme: LSB Removal/Quantization: Algorithm TX RX DQPSK 16-QAM Compression can decrease hardware complexity and cost. The number of LSBs that can [1] 1.51 1.07 TX 4 2.3 be removed without causing USBR 1.41 1.17 RX 3.5 2 distortion depends on the Elias-gamma + 1.40 0.84 Introduction and Related Work Adaptive AC modulation type. Adaptive AC 1.26 0.95 Base station architecture with compression/decompression: Elias-gamma 1.22 0.72 With higher size constellations, more bit -
Start, Step, Stop Unary Coding for Data Compression
Europaisches Patentamt J) European Patent Office GO Publication number: 0 340 041 Office europeen des brevets A2 EUROPEAN PATENT APPLICATION Application number: 89304352.1 int.ci.4-. H 03 M 7/40 G 06 F 15/40 Date of filing: 28.04.89 Priority: 29.04.88 US 187697 Applicant: XEROX CORPORATION Xerox Square - 020 Date of publication of application: Rochester New York 14644 (US) 02.11.89 Bulletin 89/44 Inventor: Fiala, Edward R. Designated Contracting States: DE FR GB 1018 Robin Way Sunnyvale California 94087 (US) Greene, Daniel H. 942 Aster Court Sunnyvale California 94086 (US) Representative : Goode, Ian Roy et al Rank Xerox Limited Patent Department 364 Euston Road London NW1 3BL (GB) @ Start, step, stop unary coding for data compression. @ Start, Step, Stop unary coding provides a family of variable length codewords for encoding compressed data, such as the copies and literals of the various textual substitution data compresion processes disclosed herein. Textual substitution data compression is, however, only one example of one type of UrDME CON MULLED data compression in which this < start, step, stop> unary coding may be used to advantage. FIG. 3 o o 3 Q. Ill Bundesdruckerei Berlin EP 0 340 041 A2 Description START, STEP, STOP UNARY CODING FOR DATA COMPRESSION This invention relates to digital data compression systems and, more particularly, to adaptive and invertible or lossless digital data compression systems. 5 Reference is made to our concurrently filed EP application (D/88068), entitled "Search Tree Data Structure Encoding for Textual Substitution Data Compression Systems". A common detailed description has been used because the inventions covered by the different applications may be combined in various ways. -
Lampiran a Listing Program
LAMPIRAN A LISTING PROGRAM Private Sub Command3_Click() If UBound(OriginalArray) = 0 Then Exit Sub End MsgBox "There is nothing to End If compress/Decompress" End Sub Else Exit Sub LastCoder = WichCompressor End If Private Sub compress_Click() End If If AutoDecodeIsOn = False Then m1.Visible = True Call Copy_Orig2Work decompress = True m2.Visible = True LastDeCoded = decompress If CompDecomp = 0 Then Exit Sub End Sub If JustLoaded = True Then If CompDecomp = 1 Then decompress = False JustLoaded = False Else Private Sub d1_Click() ReDim UsedCodecs(0) decompress = True CompDecomp = 2 End If End If Dim wktmulai As Single compression.MousePointer = If decompress = True Then MousePointerConstants.vbHourglass Dim decompress As Boolean LastUsed = wktmulai = Timer Dim Dummy As Boolean UsedCodecs(UBound(UsedCodecs)) Call DeCompress_arithmetic_Dynamic(hasil) Dim StTime As Double If JustLoaded = True Then LastUsed = 0 Label12.Caption = Timer - wktmulai & " detik" Dim Text As String If WichCompressor <> LastUsed Then compression.MousePointer = Dim LastUsed As Integer Text = "This is not compressed with ." & MousePointerConstants.vbDefault Chr(13) Call Show_Statistics(False, hasil) MsgBox Text A-1 Call afterContent(hasil) If AutoDecodeIsOn = False Then End If compression.save.Visible = True decompress = True Call Copy_Orig2Work d1.Visible = False If CompDecomp = 0 Then Exit Sub LastDeCoded = decompress d2.Visible = False If CompDecomp = 1 Then decompress = False If JustLoaded = True Then End Sub Else JustLoaded = False decompress = True ReDim UsedCodecs(0) -
Entropy Coding and Different Coding Techniques
Journal of Network Communications and Emerging Technologies (JNCET) www.jncet.org Volume 6, Issue 5, May (2016) Entropy Coding and Different Coding Techniques Sandeep Kaur Asst. Prof. in CSE Dept, CGC Landran, Mohali Sukhjeet Singh Asst. Prof. in CS Dept, TSGGSK College, Amritsar. Abstract – In today’s digital world information exchange is been 2. CODING TECHNIQUES held electronically. So there arises a need for secure transmission of the data. Besides security there are several other factors such 1. Huffman Coding- as transfer speed, cost, errors transmission etc. that plays a vital In computer science and information theory, a Huffman code role in the transmission process. The need for an efficient technique for compression of Images ever increasing because the is a particular type of optimal prefix code that is commonly raw images need large amounts of disk space seems to be a big used for lossless data compression. disadvantage during transmission & storage. This paper provide 1.1 Basic principles of Huffman Coding- a basic introduction about entropy encoding and different coding techniques. It also give comparison between various coding Huffman coding is a popular lossless Variable Length Coding techniques. (VLC) scheme, based on the following principles: (a) Shorter Index Terms – Huffman coding, DEFLATE, VLC, UTF-8, code words are assigned to more probable symbols and longer Golomb coding. code words are assigned to less probable symbols. 1. INTRODUCTION (b) No code word of a symbol is a prefix of another code word. This makes Huffman coding uniquely decodable. 1.1 Entropy Encoding (c) Every source symbol must have a unique code word In information theory an entropy encoding is a lossless data assigned to it.