Information Theory and Coding
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Lecture 24 1 Kolmogorov Complexity
6.441 Spring 2006 24-1 6.441 Transmission of Information May 18, 2006 Lecture 24 Lecturer: Madhu Sudan Scribe: Chung Chan 1 Kolmogorov complexity Shannon’s notion of compressibility is closely tied to a probability distribution. However, the probability distribution of the source is often unknown to the encoder. Sometimes, we interested in compressibility of specific sequence. e.g. How compressible is the Bible? We have the Lempel-Ziv universal compression algorithm that can compress any string without knowledge of the underlying probability except the assumption that the strings comes from a stochastic source. But is it the best compression possible for each deterministic bit string? This notion of compressibility/complexity of a deterministic bit string has been studied by Solomonoff in 1964, Kolmogorov in 1966, Chaitin in 1967 and Levin. Consider the following n-sequence 0100011011000001010011100101110111 ··· Although it may appear random, it is the enumeration of all binary strings. A natural way to compress it is to represent it by the procedure that generates it: enumerate all strings in binary, and stop at the n-th bit. The compression achieved in bits is |compression length| ≤ 2 log n + O(1) More generally, with the universal Turing machine, we can encode data to a computer program that generates it. The length of the smallest program that produces the bit string x is called the Kolmogorov complexity of x. Definition 1 (Kolmogorov complexity) For every language L, the Kolmogorov complexity of the bit string x with respect to L is KL (x) = min l(p) p:L(p)=x where p is a program represented as a bit string, L(p) is the output of the program with respect to the language L, and l(p) is the length of the program, or more precisely, the point at which the execution halts. -
Information Content and Error Analysis
43 INFORMATION CONTENT AND ERROR ANALYSIS Clive D Rodgers Atmospheric, Oceanic and Planetary Physics University of Oxford ESA Advanced Atmospheric Training Course September 15th – 20th, 2008 44 INFORMATION CONTENT OF A MEASUREMENT Information in a general qualitative sense: Conceptually, what does y tell you about x? We need to answer this to determine if a conceptual instrument design actually works • to optimise designs • Use the linear problem for simplicity to illustrate the ideas. y = Kx + ! 45 SHANNON INFORMATION The Shannon information content of a measurement of x is the change in the entropy of the • probability density function describing our knowledge of x. Entropy is defined by: • S P = P (x) log(P (x)/M (x))dx { } − Z M(x) is a measure function. We will take it to be constant. Compare this with the statistical mechanics definition of entropy: • S = k p ln p − i i i X P (x)dx corresponds to pi. 1/M (x) is a kind of scale for dx. The Shannon information content of a measurement is the change in entropy between the p.d.f. • before, P (x), and the p.d.f. after, P (x y), the measurement: | H = S P (x) S P (x y) { }− { | } What does this all mean? 46 ENTROPY OF A BOXCAR PDF Consider a uniform p.d.f in one dimension, constant in (0,a): P (x)=1/a 0 <x<a and zero outside.The entropy is given by a 1 1 S = ln dx = ln a − a a Z0 „ « Similarly, the entropy of any constant pdf in a finite volume V of arbitrary shape is: 1 1 S = ln dv = ln V − V V ZV „ « i.e the entropy is the log of the volume of state space occupied by the p.d.f. -
Chapter 4 Information Theory
Chapter Information Theory Intro duction This lecture covers entropy joint entropy mutual information and minimum descrip tion length See the texts by Cover and Mackay for a more comprehensive treatment Measures of Information Information on a computer is represented by binary bit strings Decimal numb ers can b e represented using the following enco ding The p osition of the binary digit 3 2 1 0 Bit Bit Bit Bit Decimal Table Binary encoding indicates its decimal equivalent such that if there are N bits the ith bit represents N i the decimal numb er Bit is referred to as the most signicant bit and bit N as the least signicant bit To enco de M dierent messages requires log M bits 2 Signal Pro cessing Course WD Penny April Entropy The table b elow shows the probability of o ccurrence px to two decimal places of i selected letters x in the English alphab et These statistics were taken from Mackays i b o ok on Information Theory The table also shows the information content of a x px hx i i i a e j q t z Table Probability and Information content of letters letter hx log i px i which is a measure of surprise if we had to guess what a randomly chosen letter of the English alphab et was going to b e wed say it was an A E T or other frequently o ccuring letter If it turned out to b e a Z wed b e surprised The letter E is so common that it is unusual to nd a sentence without one An exception is the page novel Gadsby by Ernest Vincent Wright in which -
Shannon Entropy and Kolmogorov Complexity
Information and Computation: Shannon Entropy and Kolmogorov Complexity Satyadev Nandakumar Department of Computer Science. IIT Kanpur October 19, 2016 This measures the average uncertainty of X in terms of the number of bits. Shannon Entropy Definition Let X be a random variable taking finitely many values, and P be its probability distribution. The Shannon Entropy of X is X 1 H(X ) = p(i) log : 2 p(i) i2X Shannon Entropy Definition Let X be a random variable taking finitely many values, and P be its probability distribution. The Shannon Entropy of X is X 1 H(X ) = p(i) log : 2 p(i) i2X This measures the average uncertainty of X in terms of the number of bits. The Triad Figure: Claude Shannon Figure: A. N. Kolmogorov Figure: Alan Turing Just Electrical Engineering \Shannon's contribution to pure mathematics was denied immediate recognition. I can recall now that even at the International Mathematical Congress, Amsterdam, 1954, my American colleagues in probability seemed rather doubtful about my allegedly exaggerated interest in Shannon's work, as they believed it consisted more of techniques than of mathematics itself. However, Shannon did not provide rigorous mathematical justification of the complicated cases and left it all to his followers. Still his mathematical intuition is amazingly correct." A. N. Kolmogorov, as quoted in [Shi89]. Kolmogorov and Entropy Kolmogorov's later work was fundamentally influenced by Shannon's. 1 Foundations: Kolmogorov Complexity - using the theory of algorithms to give a combinatorial interpretation of Shannon Entropy. 2 Analogy: Kolmogorov-Sinai Entropy, the only finitely-observable isomorphism-invariant property of dynamical systems. -
7 Image Processing: Discrete Images
7 Image Processing: Discrete Images In the previous chapter we explored linear, shift-invariant systems in the continuous two-dimensional domain. In practice, we deal with images that are both limited in extent and sampled at discrete points. The results developed so far have to be specialized, extended, and modified to be useful in this domain. Also, a few new aspects appear that must be treated carefully. The sampling theorem tells us under what circumstances a discrete set of samples can accurately represent a continuous image. We also learn what happens when the conditions for the application of this result are not met. This has significant implications for the design of imaging systems. Methods requiring transformation to the frequency domain have be- come popular, in part because of algorithms that permit the rapid compu- tation of the discrete Fourier transform. Care has to be taken, however, since these methods assume that the signal is periodic. We discuss how this requirement can be met and what happens when the assumption does not apply. 7.1 Finite Image Size In practice, images are always of finite size. Consider a rectangular image 7.1 Finite Image Size 145 of width W and height H. Then the integrals in the Fourier transform no longer need to be taken to infinity: H/2 W/2 F (u, v)= f(x, y)e−i(ux+vy) dx dy. −H/2 −W/2 Curiously, we do not need to know F (u, v) for all frequencies in order to reconstruct f(x, y). Knowing that f(x, y)=0for|x| >W/2 and |y| >H/2 provides a strong constraint. -
Understanding Shannon's Entropy Metric for Information
Understanding Shannon's Entropy metric for Information Sriram Vajapeyam [email protected] 24 March 2014 1. Overview Shannon's metric of "Entropy" of information is a foundational concept of information theory [1, 2]. Here is an intuitive way of understanding, remembering, and/or reconstructing Shannon's Entropy metric for information. Conceptually, information can be thought of as being stored in or transmitted as variables that can take on different values. A variable can be thought of as a unit of storage that can take on, at different times, one of several different specified values, following some process for taking on those values. Informally, we get information from a variable by looking at its value, just as we get information from an email by reading its contents. In the case of the variable, the information is about the process behind the variable. The entropy of a variable is the "amount of information" contained in the variable. This amount is determined not just by the number of different values the variable can take on, just as the information in an email is quantified not just by the number of words in the email or the different possible words in the language of the email. Informally, the amount of information in an email is proportional to the amount of “surprise” its reading causes. For example, if an email is simply a repeat of an earlier email, then it is not informative at all. On the other hand, if say the email reveals the outcome of a cliff-hanger election, then it is highly informative. -
Information Theory
Information Theory Professor John Daugman University of Cambridge Computer Science Tripos, Part II Michaelmas Term 2016/17 H(X,Y) I(X;Y) H(X|Y) H(Y|X) H(X) H(Y) 1 / 149 Outline of Lectures 1. Foundations: probability, uncertainty, information. 2. Entropies defined, and why they are measures of information. 3. Source coding theorem; prefix, variable-, and fixed-length codes. 4. Discrete channel properties, noise, and channel capacity. 5. Spectral properties of continuous-time signals and channels. 6. Continuous information; density; noisy channel coding theorem. 7. Signal coding and transmission schemes using Fourier theorems. 8. The quantised degrees-of-freedom in a continuous signal. 9. Gabor-Heisenberg-Weyl uncertainty relation. Optimal \Logons". 10. Data compression codes and protocols. 11. Kolmogorov complexity. Minimal description length. 12. Applications of information theory in other sciences. Reference book (*) Cover, T. & Thomas, J. Elements of Information Theory (second edition). Wiley-Interscience, 2006 2 / 149 Overview: what is information theory? Key idea: The movements and transformations of information, just like those of a fluid, are constrained by mathematical and physical laws. These laws have deep connections with: I probability theory, statistics, and combinatorics I thermodynamics (statistical physics) I spectral analysis, Fourier (and other) transforms I sampling theory, prediction, estimation theory I electrical engineering (bandwidth; signal-to-noise ratio) I complexity theory (minimal description length) I signal processing, representation, compressibility As such, information theory addresses and answers the two fundamental questions which limit all data encoding and communication systems: 1. What is the ultimate data compression? (answer: the entropy of the data, H, is its compression limit.) 2. -
Information, Entropy, and the Motivation for Source Codes
MIT 6.02 DRAFT Lecture Notes Last update: September 13, 2012 CHAPTER 2 Information, Entropy, and the Motivation for Source Codes The theory of information developed by Claude Shannon (MIT SM ’37 & PhD ’40) in the late 1940s is one of the most impactful ideas of the past century, and has changed the theory and practice of many fields of technology. The development of communication systems and networks has benefited greatly from Shannon’s work. In this chapter, we will first develop the intution behind information and formally define it as a mathematical quantity, then connect it to a related property of data sources, entropy. These notions guide us to efficiently compress a data source before communicating (or storing) it, and recovering the original data without distortion at the receiver. A key under lying idea here is coding, or more precisely, source coding, which takes each “symbol” being produced by any source of data and associates it with a codeword, while achieving several desirable properties. (A message may be thought of as a sequence of symbols in some al phabet.) This mapping between input symbols and codewords is called a code. Our focus will be on lossless source coding techniques, where the recipient of any uncorrupted mes sage can recover the original message exactly (we deal with corrupted messages in later chapters). ⌅ 2.1 Information and Entropy One of Shannon’s brilliant insights, building on earlier work by Hartley, was to realize that regardless of the application and the semantics of the messages involved, a general definition of information is possible. -
Paper 1 Jian Li Kolmold
KolmoLD: Data Modelling for the Modern Internet* Dmitry Borzov, Huawei Canada Tim Tingqiu Yuan, Huawei Mikhail Ignatovich, Huawei Canada Jian Li, Futurewei *work performed before May 2019 Challenges: Peak Traffic Composition 73% 26% Content: sizable, faned-out, static Streaming Services Everything else (Netflix, Hulu, Software File storage (Instant Messaging, YouTube, Spotify) distribution services VoIP, Social Media) [1] Source: Sandvine Global Internet Phenomena reports for 2009, 2010, 2011, 2012, 2013, 2015, 2016, October 2018 [1] https://qz.com/1001569/the-cdn-heavy-internet-in-rich-countries-will-be-unrecognizable-from-the-rest-of-the-worlds-in-five-years/ Technologies to define the revolution of the internet ChunkStream Founded in 2016 Video codec Founded in 2014 Content-addressable Based on a 2014 MIT Browser-targeted Runtime Content-addressable network protocol based research paper network protocol based on cryptohash naming on cryptohash naming scheme Implemented and supported by all major scheme Based on the Open source project cryptohash naming browsers, an IETF standard Founding company is a P2P project scheme YCombinator graduate YCombinator graduate with backing of high profile SV investors Our Proposal: A data model for interoperable protocols KolmoLD Content addressing through hashes has become a widely-used means of Addressable: connecting layer, inspired by connecting data in distributed the principles of Kolmogorov complexity systems, from the blockchains that theory run your favorite cryptocurrencies, to the commits that back your code, to Compossible: sending data as code, where the web’s content at large. code efficiency is theoretically bounded by Kolmogorov complexity Yet, whilst all of these tools rely on Computable: sandboxed computability by some common primitives, their treating data as code specific underlying data structures are not interoperable. -
Arxiv:1801.05832V2 [Cs.DS]
Efficient Computation of the 8-point DCT via Summation by Parts D. F. G. Coelho∗ R. J. Cintra† V. S. Dimitrov‡ Abstract This paper introduces a new fast algorithm for the 8-point discrete cosine transform (DCT) based on the summation-by-parts formula. The proposed method converts the DCT matrix into an alternative transformation matrix that can be decomposed into sparse matrices of low multiplicative complexity. The method is capable of scaled and exact DCT computation and its associated fast algorithm achieves the theoretical minimal multiplicative complexity for the 8-point DCT. Depending on the nature of the input signal simplifications can be introduced and the overall complexity of the proposed algorithm can be further reduced. Several types of input signal are analyzed: arbitrary, null mean, accumulated, and null mean/accumulated signal. The proposed tool has potential application in harmonic detection, image enhancement, and feature extraction, where input signal DC level is discarded and/or the signal is required to be integrated. Keywords DCT, Fast Algorithms, Image Processing 1 Introduction Discrete transforms play a central role in signal processing. Noteworthy methods include trigonometric transforms— such as the discrete Fourier transform (DFT) [1], discrete Hartley transform (DHT) [1], discrete cosine transform (DCT) [2], and discrete sine transform (DST) [2]—as well as the Haar and Walsh-Hadamard transforms [3]. Among these methods, the DCT has been applied in several practical contexts: noise reduction [4], watermarking methods [5], image/video compression techniques [2], and harmonic detection [2], to cite a few. In fact, when processing signals modeled as a stationary Markov-1 type random process, the DCT behaves as the asymptotic case of the optimal Karhunen–Lo`eve transform in terms of data decorrelation [2]. -
Maximizing T-Complexity 1. Introduction
Fundamenta Informaticae XX (2015) 1–19 1 DOI 10.3233/FI-2012-0000 IOS Press Maximizing T-complexity Gregory Clark U.S. Department of Defense gsclark@ tycho. ncsc. mil Jason Teutsch Penn State University teutsch@ cse. psu. edu Abstract. We investigate Mark Titchener’s T-complexity, an algorithm which measures the infor- mation content of finite strings. After introducing the T-complexity algorithm, we turn our attention to a particular class of “simple” finite strings. By exploiting special properties of simple strings, we obtain a fast algorithm to compute the maximum T-complexity among strings of a given length, and our estimates of these maxima show that T-complexity differs asymptotically from Kolmogorov complexity. Finally, we examine how closely de Bruijn sequences resemble strings with high T- complexity. 1. Introduction How efficiently can we generate random-looking strings? Random strings play an important role in data compression since such strings, characterized by their lack of patterns, do not compress well. For prac- tical compression applications, compression must take place in a reasonable amount of time (typically linear or nearly linear time). The degree of success achieved with the classical compression algorithms of Lempel and Ziv [9, 21, 22] and Welch [19] depend on the complexity of the input string; the length of the compressed string gives a measure of the original string’s complexity. In the same monograph in which Lempel and Ziv introduced the first of these algorithms [9], they gave an example of a succinctly describable class of strings which yield optimally poor compression up to a logarithmic factor, namely the class of lexicographically least de Bruijn sequences for each length. -
Comparison of FFT, DCT, DWT, WHT Compression Techniques on Electrocardiogram & Photoplethysmography Signals
Special Issue of International Journal of Computer Applications (0975 – 8887) International Conference on Computing, Communication and Sensor Network (CCSN) 2012 Comparison of FFT, DCT, DWT, WHT Compression Techniques on Electrocardiogram & Photoplethysmography Signals Anamitra Bardhan Roy Debasmita Dey Devmalya Banerjee Programmer Analyst Trainee, B.Tech. Student, Dept. of CSE, JIS Asst. Professor, Dept. of EIE Cognizant Technology Solution, College of Engineering College of Engineering, WB, India Kolkata, WB, India Kolkata, WB, India, Bidisha Mohanty B.Tech. Student, Dept. of ECE, JIS College of Engineering, WB, India. ABSTRACT media requires high security and authentication [1]. This Compression technique plays an important role indiagnosis, concept of telemedicine basically and foremost needs image prognosis and analysis of ischemic heart diseases.It is also compression as a vital part of image processing techniques to preferable for its fast data sending capability in the field of manage with the data capacity to transfer within a short and telemedicine.Various techniques have been proposed over stipulated time interval. The therapeutic remedies are derived years for compression. Among those Discrete Cosine from the minutiae’s of the biomedical signals shared Transformation(DCT), Discrete Wavelet worldwide through telelinking. This communication of Transformation(DWT), Fast Fourier Transformation(FFT) exchanging medical data through wireless media requires high andWalsh Hadamard Transformation(WHT) are mostly level of compression techniques to cope up with the restricted used.In this paper a comparative study of FFT, DCT, DWT, data transfer capacity and rate. Compression of signals and and WHT is proposed using ECG and PPG signal, images can cause distortion in them. As the medical signals whichshows a certain relation between them as discussed in and images contains and relays information required for previous papers[1].