Evaluation of Image Compression Algorithms for Electronic Shelf Labels
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Minimax Redundancy for Markov Chains with Large State Space
Minimax redundancy for Markov chains with large state space Kedar Shriram Tatwawadi, Jiantao Jiao, Tsachy Weissman May 8, 2018 Abstract For any Markov source, there exist universal codes whose normalized codelength approaches the Shannon limit asymptotically as the number of samples goes to infinity. This paper inves- tigates how fast the gap between the normalized codelength of the “best” universal compressor and the Shannon limit (i.e. the compression redundancy) vanishes non-asymptotically in terms of the alphabet size and mixing time of the Markov source. We show that, for Markov sources (2+c) whose relaxation time is at least 1 + √k , where k is the state space size (and c> 0 is a con- stant), the phase transition for the number of samples required to achieve vanishing compression redundancy is precisely Θ(k2). 1 Introduction For any data source that can be modeled as a stationary ergodic stochastic process, it is well known in the literature of universal compression that there exist compression algorithms without any knowledge of the source distribution, such that its performance can approach the fundamental limit of the source, also known as the Shannon entropy, as the number of observations tends to infinity. The existence of universal data compressors has spurred a huge wave of research around it. A large fraction of practical lossless compressors are based on the Lempel–Ziv algorithms [ZL77, ZL78] and their variants, and the normalized codelength of a universal source code is also widely used to measure the compressibility of the source, which is based on the idea that the normalized codelength is “close” to the true entropy rate given a moderate number of samples. -
Digital Communication Systems 2.2 Optimal Source Coding
Digital Communication Systems EES 452 Asst. Prof. Dr. Prapun Suksompong [email protected] 2. Source Coding 2.2 Optimal Source Coding: Huffman Coding: Origin, Recipe, MATLAB Implementation 1 Examples of Prefix Codes Nonsingular Fixed-Length Code Shannon–Fano code Huffman Code 2 Prof. Robert Fano (1917-2016) Shannon Award (1976 ) Shannon–Fano Code Proposed in Shannon’s “A Mathematical Theory of Communication” in 1948 The method was attributed to Fano, who later published it as a technical report. Fano, R.M. (1949). “The transmission of information”. Technical Report No. 65. Cambridge (Mass.), USA: Research Laboratory of Electronics at MIT. Should not be confused with Shannon coding, the coding method used to prove Shannon's noiseless coding theorem, or with Shannon–Fano–Elias coding (also known as Elias coding), the precursor to arithmetic coding. 3 Claude E. Shannon Award Claude E. Shannon (1972) Elwyn R. Berlekamp (1993) Sergio Verdu (2007) David S. Slepian (1974) Aaron D. Wyner (1994) Robert M. Gray (2008) Robert M. Fano (1976) G. David Forney, Jr. (1995) Jorma Rissanen (2009) Peter Elias (1977) Imre Csiszár (1996) Te Sun Han (2010) Mark S. Pinsker (1978) Jacob Ziv (1997) Shlomo Shamai (Shitz) (2011) Jacob Wolfowitz (1979) Neil J. A. Sloane (1998) Abbas El Gamal (2012) W. Wesley Peterson (1981) Tadao Kasami (1999) Katalin Marton (2013) Irving S. Reed (1982) Thomas Kailath (2000) János Körner (2014) Robert G. Gallager (1983) Jack KeilWolf (2001) Arthur Robert Calderbank (2015) Solomon W. Golomb (1985) Toby Berger (2002) Alexander S. Holevo (2016) William L. Root (1986) Lloyd R. Welch (2003) David Tse (2017) James L. -
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 ............................................................................................................................... -
Principles of Communications ECS 332
Principles of Communications ECS 332 Asst. Prof. Dr. Prapun Suksompong (ผศ.ดร.ประพันธ ์ สขสมปองุ ) [email protected] 1. Intro to Communication Systems Office Hours: Check Google Calendar on the course website. Dr.Prapun’s Office: 6th floor of Sirindhralai building, 1 BKD 2 Remark 1 If the downloaded file crashed your device/browser, try another one posted on the course website: 3 Remark 2 There is also three more sections from the Appendices of the lecture notes: 4 Shannon's insight 5 “The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.” Shannon, Claude. A Mathematical Theory Of Communication. (1948) 6 Shannon: Father of the Info. Age Documentary Co-produced by the Jacobs School, UCSD- TV, and the California Institute for Telecommunic ations and Information Technology 7 [http://www.uctv.tv/shows/Claude-Shannon-Father-of-the-Information-Age-6090] [http://www.youtube.com/watch?v=z2Whj_nL-x8] C. E. Shannon (1916-2001) Hello. I'm Claude Shannon a mathematician here at the Bell Telephone laboratories He didn't create the compact disc, the fax machine, digital wireless telephones Or mp3 files, but in 1948 Claude Shannon paved the way for all of them with the Basic theory underlying digital communications and storage he called it 8 information theory. C. E. Shannon (1916-2001) 9 https://www.youtube.com/watch?v=47ag2sXRDeU C. E. Shannon (1916-2001) One of the most influential minds of the 20th century yet when he died on February 24, 2001, Shannon was virtually unknown to the public at large 10 C. -
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. -
A Survey on Different Compression Techniques Algorithm for Data Compression Ihardik Jani, Iijeegar Trivedi IC
International Journal of Advanced Research in ISSN : 2347 - 8446 (Online) Computer Science & Technology (IJARCST 2014) Vol. 2, Issue 3 (July - Sept. 2014) ISSN : 2347 - 9817 (Print) A Survey on Different Compression Techniques Algorithm for Data Compression IHardik Jani, IIJeegar Trivedi IC. U. Shah University, India IIS. P. University, India Abstract Compression is useful because it helps us to reduce the resources usage, such as data storage space or transmission capacity. Data Compression is the technique of representing information in a compacted form. The actual aim of data compression is to be reduced redundancy in stored or communicated data, as well as increasing effectively data density. The data compression has important tool for the areas of file storage and distributed systems. To desirable Storage space on disks is expensively so a file which occupies less disk space is “cheapest” than an uncompressed files. The main purpose of data compression is asymptotically optimum data storage for all resources. The field data compression algorithm can be divided into different ways: lossless data compression and optimum lossy data compression as well as storage areas. Basically there are so many Compression methods available, which have a long list. In this paper, reviews of different basic lossless data and lossy compression algorithms are considered. On the basis of these techniques researcher have tried to purpose a bit reduction algorithm used for compression of data which is based on number theory system and file differential technique. The statistical coding techniques the algorithms such as Shannon-Fano Coding, Huffman coding, Adaptive Huffman coding, Run Length Encoding and Arithmetic coding are considered. -
IEEE Information Theory Society Newsletter
IEEE Information Theory Society Newsletter Vol. 59, No. 3, September 2009 Editor: Tracey Ho ISSN 1059-2362 Optimal Estimation XXX Shannon lecture, presented in 2009 in Seoul, South Korea (extended abstract) J. Rissanen 1 Prologue 2 Modeling Problem The fi rst quest for optimal estimation by Fisher, [2], Cramer, The modeling problem begins with a set of observed data 5 5 5 c 6 Rao and others, [1], dates back to over half a century and has Y yt:t 1, 2, , n , often together with other so-called 5 51 c26 changed remarkably little. The covariance of the estimated pa- explanatory data Y, X yt, x1,t, x2,t, . The objective is rameters was taken as the quality measure of estimators, for to learn properties in Y expressed by a set of distributions which the main result, the Cramer-Rao inequality, sets a lower as models bound. It is reached by the maximum likelihood (ML) estima- 5 1 u 26 tor for a restricted subclass of models and asymptotically for f Y|Xs; , s . a wider class. The covariance, which is just one property of u5u c u models, is too weak a measure to permit extension to estima- Here s is a structure parameter and 1, , k1s2 real- valued tion of the number of parameters, which is handled by various parameters, whose number depends on the structure. The ad hoc criteria too numerous to list here. structure is simply a subset of the models. Typically it is used to indicate the most important variables in X. (The traditional Soon after I had studied Shannon’s formal defi nition of in- name for the set of models is ‘likelihood function’ although no formation in random variables and his other remarkable per- such concept exists in probability theory.) formance bounds for communication, [4], I wanted to apply them to other fi elds – in particular to estimation and statis- The most important problem is the selection of the model tics in general. -
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. -
On the Coding Gain of Dynamic Huffman Coding Applied to a Wavelet-Based Perceptual Audio Coder
ON THE CODING GAIN OF DYNAMIC HUFFMAN CODING APPLIED TO A WAVELET-BASED PERCEPTUAL AUDIO CODER N. Ruiz Reyes1, M. Rosa Zurera2, F. López Ferreras2, P. Jarabo Amores2, and P. Vera Candeas1 1Departamento de Electrónica, Universidad de Jaén, Escuela Universitaria Politénica, 23700 Linares, Jaén, SPAIN, e-mail: [email protected] 2Dpto. de Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Escuela Politécnica 28871 Alcalá de Henares, Madrid, SPAIN, e-mail: [email protected] ABSTRACT The last one, the ISO/MPEG-4 standard, is composed of several speech and audio coders supporting bit rates This paper evaluates the coding gain of using a dynamic from 2 to 64 kbps per channel. ISO/MPEG-4 includes Huffman entropy coder in an audio coder that uses a the already proposed AAC standard, which provides high wavelet-packet decomposition that is close to the sub- quality audio coding at bit rates of 64 kbps per channel. band decomposition made by the human ear. The sub- Parallel to the definition of the ISO/MPEG standards, band audio signals are modeled as samples of a station- several audio coding algorithms have been proposed that ary random process with laplacian probability density use the wavelet transform as the tool to decompose the function because experimental results indicate that the audio signal. The most promising results correspond to highest coding efficiency is obtained in that case. We adapted wavelet-based audio coders. Probably, the most have also studied how the entropy coding gain varies cited is the one designed by Sinha and Tewfik [2], a high with the band index. -
Itnl0908.Pdf
itNL0908.qxd 8/13/08 8:34 AM Page 1 IEEE Information Theory Society Newsletter Vol. 58, No. 3, September 2008 Editor: Daniela Tuninetti ISSN 1059-2362 Annual IT Awards Announced The principal annual awards of the Information Theory Society of the U.S. National Academy of Engineering (2007 to date). He were announced at the Toronto ISIT. The 2009 Shannon Award will be General Co-Chair of the 2009 ISIT in Seoul. goes to Jorma Rissanen. The 2008 Wyner Award goes to Vincent Poor. The winners of the 2008 IT Paper Award are two 2006 IT The Information Theory Society Paper Award is given annually Transactions papers on compressed sensing by David Donoho to an outstanding publication in the fields of interest to the and by Emmanuel Candes and Terence Tao, respectively (with Society appearing anywhere during the preceding two calendar acknowledgment of an earlier paper by Candes, Romberg and years. The winners of the 2008 award are "Compressed sensing," Tao). The winner of the 2008 Joint IT/ComSoc Paper Award is a by David Donoho, which appeared in the April 2006 IEEE Communications Transactions paper on Accumulate-Repeat- Transactions on Information Theory, and "Near-optimal signal Accumulate Codes by Aliazam Abbasfar, Dariush Divsalar and recovery from random projections: universal encoding strate- Kung Yao. Three student authors of ISIT papers received ISIT gies," by Emmanuel Candes and Terence Tao, which appeared Student Paper Awards: Paul Cuff of Stanford, Satish Korada of in the December 2006 Transactions. These two overlapping EPFL, and Yury Polyanskiy of Princeton. Finally, the 2008 papers are the foundations of the exciting new field of com- Chapter of the Year Award was presented to the Kitchener- pressed sensing. -
Applying Compression to a Game's Network Protocol
Applying Compression to a Game's Network Protocol Mikael Hirki Aalto University, Finland [email protected] Abstract. This report presents the results of applying different com- pression algorithms to the network protocol of an online game. The al- gorithm implementations compared are zlib, liblzma and my own imple- mentation based on LZ77 and a variation of adaptive Huffman coding. The comparison data was collected from the game TomeNET. The re- sults show that adaptive coding is especially useful for compressing large amounts of very small packets. 1 Introduction The purpose of this project report is to present and discuss the results of applying compression to the network protocol of a multi-player online game. This poses new challenges for the compression algorithms and I have also developed my own algorithm that tries to tackle some of these. Networked games typically follow a client-server model where multiple clients connect to a single server. The server is constantly streaming data that contains game updates to the clients as long as they are connected. The client will also send data to the about the player's actions. This report focuses on compressing the data stream from server to client. The amount of data sent from client to server is likely going to be very small and therefore it would not be of any interest to compress it. Many potential benefits may be achieved by compressing the game data stream. A server with limited bandwidth could theoretically serve more clients. Compression will lower the bandwidth requirements for each individual client. Compression will also likely reduce the number of packets required to transmit arXiv:1206.2362v1 [cs.IT] 28 May 2012 larger data bursts.