Comparison of Image Compressions: Analog Transformations P
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A Survey Paper on Different Speech Compression Techniques
Vol-2 Issue-5 2016 IJARIIE-ISSN (O)-2395-4396 A Survey Paper on Different Speech Compression Techniques Kanawade Pramila.R1, Prof. Gundal Shital.S2 1 M.E. Electronics, Department of Electronics Engineering, Amrutvahini College of Engineering, Sangamner, Maharashtra, India. 2 HOD in Electronics Department, Department of Electronics Engineering , Amrutvahini College of Engineering, Sangamner, Maharashtra, India. ABSTRACT This paper describes the different types of speech compression techniques. Speech compression can be divided into two main types such as lossless and lossy compression. This survey paper has been written with the help of different types of Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression etc. Compression is nothing but reducing size of data with considering memory size. Speech compression means voiced signal compress for different application such as high quality database of speech signals, multimedia applications, music database and internet applications. Today speech compression is very useful in our life. The main purpose or aim of speech compression is to compress any type of audio that is transfer over the communication channel, because of the limited channel bandwidth and data storage capacity and low bit rate. The use of lossless and lossy techniques for speech compression means that reduced the numbers of bits in the original information. By the use of lossless data compression there is no loss in the original information but while using lossy data compression technique some numbers of bits are loss. Keyword: - Bit rate, Compression, Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression. 1. INTRODUCTION -1 Speech compression is use in the encoding system. -
Arxiv:2004.10531V1 [Cs.OH] 8 Apr 2020
ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, increasing per- formance and enhancing it and improving its interaction with other data analy- sis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are sig- nificant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space. 1 Introduction In the past years LHC experiments are commissioned and now manages about an exabyte of storage for analysis purposes, approximately half of which is used for archival purposes, and half is used for traditional disk storage. Meanwhile for HL-LHC storage requirements per year are expected to be increased by factor 10 [1]. arXiv:2004.10531v1 [cs.OH] 8 Apr 2020 Looking at these predictions, we would like to state that storage will remain one of the major cost drivers and at the same time the bottlenecks for HEP computing. -
An Overview of Wavelet Transform Concepts and Applications
An overview of wavelet transform concepts and applications Christopher Liner, University of Houston February 26, 2010 Abstract The continuous wavelet transform utilizing a complex Morlet analyzing wavelet has a close connection to the Fourier transform and is a powerful analysis tool for decomposing broadband wavefield data. A wide range of seismic wavelet applications have been reported over the last three decades, and the free Seismic Unix processing system now contains a code (succwt) based on the work reported here. Introduction The continuous wavelet transform (CWT) is one method of investigating the time-frequency details of data whose spectral content varies with time (non-stationary time series). Moti- vation for the CWT can be found in Goupillaud et al. [12], along with a discussion of its relationship to the Fourier and Gabor transforms. As a brief overview, we note that French geophysicist J. Morlet worked with non- stationary time series in the late 1970's to find an alternative to the short-time Fourier transform (STFT). The STFT was known to have poor localization in both time and fre- quency, although it was a first step beyond the standard Fourier transform in the analysis of such data. Morlet's original wavelet transform idea was developed in collaboration with the- oretical physicist A. Grossmann, whose contributions included an exact inversion formula. A series of fundamental papers flowed from this collaboration [16, 12, 13], and connections were soon recognized between Morlet's wavelet transform and earlier methods, including harmonic analysis, scale-space representations, and conjugated quadrature filters. For fur- ther details, the interested reader is referred to Daubechies' [7] account of the early history of the wavelet transform. -
A Low Bit Rate Audio Codec Using Wavelet Transform
Navpreet Singh, Mandeep Kaur,Rajveer Kaur / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 4, Jul-Aug 2013, pp.2222-2228 An Enhanced Low Bit Rate Audio Codec Using Discrete Wavelet Transform Navpreet Singh1, Mandeep Kaur2, Rajveer Kaur3 1,2(M. tech Students, Department of ECE, Guru kashi University, Talwandi Sabo(BTI.), Punjab, INDIA 3(Asst. Prof. Department of ECE, Guru Kashi University, Talwandi Sabo (BTI.), Punjab,INDIA Abstract Audio coding is the technology to represent information and perceptually irrelevant signal audio in digital form with as few bits as possible components can be separated and later removed. This while maintaining the intelligibility and quality class includes techniques such as subband coding; required for particular application. Interest in audio transform coding, critical band analysis, and masking coding is motivated by the evolution to digital effects. The second class takes advantage of the communications and the requirement to minimize statistical redundancy in audio signal and applies bit rate, and hence conserve bandwidth. There is some form of digital encoding. Examples of this class always a tradeoff between lowering the bit rate and include entropy coding in lossless compression and maintaining the delivered audio quality and scalar/vector quantization in lossy compression [1]. intelligibility. The wavelet transform has proven to Digital audio compression allows the be a valuable tool in many application areas for efficient storage and transmission of audio data. The analysis of nonstationary signals such as image and various audio compression techniques offer different audio signals. In this paper a low bit rate audio levels of complexity, compressed audio quality, and codec algorithm using wavelet transform has been amount of data compression. -
Lossless Compression of Audio Data
CHAPTER 12 Lossless Compression of Audio Data ROBERT C. MAHER OVERVIEW Lossless data compression of digital audio signals is useful when it is necessary to minimize the storage space or transmission bandwidth of audio data while still maintaining archival quality. Available techniques for lossless audio compression, or lossless audio packing, generally employ an adaptive waveform predictor with a variable-rate entropy coding of the residual, such as Huffman or Golomb-Rice coding. The amount of data compression can vary considerably from one audio waveform to another, but ratios of less than 3 are typical. Several freeware, shareware, and proprietary commercial lossless audio packing programs are available. 12.1 INTRODUCTION The Internet is increasingly being used as a means to deliver audio content to end-users for en tertainment, education, and commerce. It is clearly advantageous to minimize the time required to download an audio data file and the storage capacity required to hold it. Moreover, the expec tations of end-users with regard to signal quality, number of audio channels, meta-data such as song lyrics, and similar additional features provide incentives to compress the audio data. 12.1.1 Background In the past decade there have been significant breakthroughs in audio data compression using lossy perceptual coding [1]. These techniques lower the bit rate required to represent the signal by establishing perceptual error criteria, meaning that a model of human hearing perception is Copyright 2003. Elsevier Science (USA). 255 AU rights reserved. 256 PART III / APPLICATIONS used to guide the elimination of excess bits that can be either reconstructed (redundancy in the signal) orignored (inaudible components in the signal). -
Image Compression Techniques by Using Wavelet Transform
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by International Institute for Science, Technology and Education (IISTE): E-Journals Journal of Information Engineering and Applications www.iiste.org ISSN 2224-5782 (print) ISSN 2225-0506 (online) Vol 2, No.5, 2012 Image Compression Techniques by using Wavelet Transform V. V. Sunil Kumar 1* M. Indra Sena Reddy 2 1. Dept. of CSE, PBR Visvodaya Institute of Tech & Science, Kavali, Nellore (Dt), AP, INDIA 2. School of Computer science & Enginering, RGM College of Engineering & Tech. Nandyal, A.P, India * E-mail of the corresponding author: [email protected] Abstract This paper is concerned with a certain type of compression techniques by using wavelet transforms. Wavelets are used to characterize a complex pattern as a series of simple patterns and coefficients that, when multiplied and summed, reproduce the original pattern. The data compression schemes can be divided into lossless and lossy compression. Lossy compression generally provides much higher compression than lossless compression. Wavelets are a class of functions used to localize a given signal in both space and scaling domains. A MinImage was originally created to test one type of wavelet and the additional functionality was added to Image to support other wavelet types, and the EZW coding algorithm was implemented to achieve better compression. Keywords: Wavelet Transforms, Image Compression, Lossless Compression, Lossy Compression 1. Introduction Digital images are widely used in computer applications. Uncompressed digital images require considerable storage capacity and transmission bandwidth. Efficient image compression solutions are becoming more critical with the recent growth of data intensive, multimedia based web applications. -
JPEG2000: Wavelets in Image Compression
EE678 WAVELETS APPLICATION ASSIGNMENT 1 JPEG2000: Wavelets In Image Compression Group Members: Qutubuddin Saifee [email protected] 01d07009 Ankur Gupta [email protected] 01d070013 Nishant Singh [email protected] 01d07019 Abstract During the past decade, with the birth of wavelet theory and multiresolution analysis, image processing techniques based on wavelet transform have been extensively studied and tremendously improved. JPEG 2000 uses wavelet transform and provides an integrated toolbox to better address increasing needs for compression. In this report, we study the basic concepts of JPEG2000, the LeGall 5/3 and Daubechies 9/7 wavelets used in it and finally Embedded zerotree wavelet coding and Set Partitioning in Hierarchical Trees. Index Terms Wavelets, JPEG2000, Image Compression, LeGall, Daubechies, EZW, SPIHT. I. INTRODUCTION I NCE the mid 1980s, members from both the International Telecommunications Union (ITU) and the International SOrganization for Standardization (ISO) have been working together to establish a joint international standard for the compression of grayscale and color still images. This effort has been known as JPEG, the Joint Photographic Experts Group. The process was such that, after evaluating a number of coding schemes, the JPEG members selected a discrete cosine transform( DCT)-based method in 1988. From 1988 to 1990, the JPEG group continued its work by simulating, testing and documenting the algorithm. JPEG became Draft International Standard (DIS) in 1991 and International Standard (IS) in 1992. With the continual expansion of multimedia and Internet applications, the needs and requirements of the technologies used grew and evolved. In March 1997, a new call for contributions was launched for the development of a new standard for the compression of still images, the JPEG2000 standard. -
Wavelet Transform for JPG, BMP & TIFF
Wavelet Transform for JPG, BMP & TIFF Samir H. Abdul-Jauwad Mansour I. AlJaroudi Electrical Engineering Department, Electrical Engineering Department, King Fahd University of Petroleum & King Fahd University of Petroleum & Minerals Minerals Dhahran 31261, Saudi Arabia Dhahran 31261, Saudi Arabia ABSTRACT This paper presents briefly both wavelet transform and inverse wavelet transform for three different images format; JPG, BMP and TIFF. After brief historical view of wavelet, an introduction will define wavelets transform. By narrowing the scope, it emphasizes the discrete wavelet transform (DWT). A practical example of DWT is shown, by choosing three images and applying MATLAB code for 1st level and 2nd level wavelet decomposition then getting the inverse wavelet transform for the three images. KEYWORDS: Wavelet Transform, JPG, BMP, TIFF, MATLAB. __________________________________________________________________ Correspondence: Dr. Samir H. Abdul-Jauwad, Electrical Engineering Department, King Fahd University of Petroleum & Mineral, Dhahran 1261, Saudi Arabia. Email: [email protected] Phone: +96638602500 Fax: +9663860245 INTRODUCTION In 1807, theories of frequency analysis by Joseph Fourier were the main lead to wavelet. However, wavelet was first appeared in an appendix to the thesis of A. Haar in 1909 [1]. Compact support was one property of the Haar wavelet which means that it vanishes outside of a finite interval. Unfortunately, Haar wavelets have some limits, because they are not continuously differentiable. In 1930, work done separately by scientists for representing the functions by using scale-varying basis functions was the key to understanding wavelets. In 1980, Grossman and Morlet, a physicist and an engineer, provided a way of thinking for wavelets based on physical intuition through defining wavelets in the context of quantum physics [3]. -
The Wavelet Tutorial Second Edition Part I by Robi Polikar
THE WAVELET TUTORIAL SECOND EDITION PART I BY ROBI POLIKAR FUNDAMENTAL CONCEPTS & AN OVERVIEW OF THE WAVELET THEORY Welcome to this introductory tutorial on wavelet transforms. The wavelet transform is a relatively new concept (about 10 years old), but yet there are quite a few articles and books written on them. However, most of these books and articles are written by math people, for the other math people; still most of the math people don't know what the other math people are 1 talking about (a math professor of mine made this confession). In other words, majority of the literature available on wavelet transforms are of little help, if any, to those who are new to this subject (this is my personal opinion). When I first started working on wavelet transforms I have struggled for many hours and days to figure out what was going on in this mysterious world of wavelet transforms, due to the lack of introductory level text(s) in this subject. Therefore, I have decided to write this tutorial for the ones who are new to the topic. I consider myself quite new to the subject too, and I have to confess that I have not figured out all the theoretical details yet. However, as far as the engineering applications are concerned, I think all the theoretical details are not necessarily necessary (!). In this tutorial I will try to give basic principles underlying the wavelet theory. The proofs of the theorems and related equations will not be given in this tutorial due to the simple assumption that the intended readers of this tutorial do not need them at this time. -
Data Compression
Data Compression Data Compression Compression reduces the size of a file: ! To save space when storing it. ! To save time when transmitting it. ! Most files have lots of redundancy. Who needs compression? ! Moore's law: # transistors on a chip doubles every 18-24 months. ! Parkinson's law: data expands to fill space available. ! Text, images, sound, video, . All of the books in the world contain no more information than is Reference: Chapter 22, Algorithms in C, 2nd Edition, Robert Sedgewick. broadcast as video in a single large American city in a single year. Reference: Introduction to Data Compression, Guy Blelloch. Not all bits have equal value. -Carl Sagan Basic concepts ancient (1950s), best technology recently developed. Robert Sedgewick and Kevin Wayne • Copyright © 2005 • http://www.Princeton.EDU/~cos226 2 Applications of Data Compression Encoding and Decoding hopefully uses fewer bits Generic file compression. Message. Binary data M we want to compress. ! Files: GZIP, BZIP, BOA. Encode. Generate a "compressed" representation C(M). ! Archivers: PKZIP. Decode. Reconstruct original message or some approximation M'. ! File systems: NTFS. Multimedia. M Encoder C(M) Decoder M' ! Images: GIF, JPEG. ! Sound: MP3. ! Video: MPEG, DivX™, HDTV. Compression ratio. Bits in C(M) / bits in M. Communication. ! ITU-T T4 Group 3 Fax. Lossless. M = M', 50-75% or lower. ! V.42bis modem. Ex. Natural language, source code, executables. Databases. Google. Lossy. M ! M', 10% or lower. Ex. Images, sound, video. 3 4 Ancient Ideas Run-Length Encoding Ancient ideas. Natural encoding. (19 " 51) + 6 = 975 bits. ! Braille. needed to encode number of characters per line ! Morse code. -
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. -
Discrete Generalizations of the Nyquist-Shannon Sampling Theorem
DISCRETE SAMPLING: DISCRETE GENERALIZATIONS OF THE NYQUIST-SHANNON SAMPLING THEOREM A DISSERTATION SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY William Wu June 2010 © 2010 by William David Wu. All Rights Reserved. Re-distributed by Stanford University under license with the author. This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/ This dissertation is online at: http://purl.stanford.edu/rj968hd9244 ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Brad Osgood, Primary Adviser I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. Thomas Cover I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. John Gill, III Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives. iii Abstract 80, 0< 11 17 æ 80, 1< 5 81, 0< 81, 1< 12 83, 1< 84, 0< 82, 1< 83, 4< 82, 3< 15 7 6 83, 2< 83, 3< 84, 8< 83, 6< 83, 7< 13 3 æ æ æ æ æ æ æ æ æ æ 9 -5 -4 -3 -2 -1 1 2 3 4 5 84, 4< 84, 13< 84, 14< 1 HIS DISSERTATION lays a foundation for the theory of reconstructing signals from T finite-dimensional vector spaces using a minimal number of samples.