Introduction to Data Compression, by Guy E. Blelloch
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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. -
Digital Speech Processing— Lecture 17
Digital Speech Processing— Lecture 17 Speech Coding Methods Based on Speech Models 1 Waveform Coding versus Block Processing • Waveform coding – sample-by-sample matching of waveforms – coding quality measured using SNR • Source modeling (block processing) – block processing of signal => vector of outputs every block – overlapped blocks Block 1 Block 2 Block 3 2 Model-Based Speech Coding • we’ve carried waveform coding based on optimizing and maximizing SNR about as far as possible – achieved bit rate reductions on the order of 4:1 (i.e., from 128 Kbps PCM to 32 Kbps ADPCM) at the same time achieving toll quality SNR for telephone-bandwidth speech • to lower bit rate further without reducing speech quality, we need to exploit features of the speech production model, including: – source modeling – spectrum modeling – use of codebook methods for coding efficiency • we also need a new way of comparing performance of different waveform and model-based coding methods – an objective measure, like SNR, isn’t an appropriate measure for model- based coders since they operate on blocks of speech and don’t follow the waveform on a sample-by-sample basis – new subjective measures need to be used that measure user-perceived quality, intelligibility, and robustness to multiple factors 3 Topics Covered in this Lecture • Enhancements for ADPCM Coders – pitch prediction – noise shaping • Analysis-by-Synthesis Speech Coders – multipulse linear prediction coder (MPLPC) – code-excited linear prediction (CELP) • Open-Loop Speech Coders – two-state excitation -
Word-Based Text Compression
WORD-BASED TEXT COMPRESSION Jan Platoš, Jiří Dvorský Department of Computer Science VŠB – Technical University of Ostrava, Czech Republic {jan.platos.fei, jiri.dvorsky}@vsb.cz ABSTRACT Today there are many universal compression algorithms, but in most cases is for specific data better using specific algorithm - JPEG for images, MPEG for movies, etc. For textual documents there are special methods based on PPM algorithm or methods with non-character access, e.g. word-based compression. In the past, several papers describing variants of word- based compression using Huffman encoding or LZW method were published. The subject of this paper is the description of a word-based compression variant based on the LZ77 algorithm. The LZ77 algorithm and its modifications are described in this paper. Moreover, various ways of sliding window implementation and various possibilities of output encoding are described, as well. This paper also includes the implementation of an experimental application, testing of its efficiency and finding the best combination of all parts of the LZ77 coder. This is done to achieve the best compression ratio. In conclusion there is comparison of this implemented application with other word-based compression programs and with other commonly used compression programs. Key Words: LZ77, word-based compression, text compression 1. Introduction Data compression is used more and more the text compression. In the past, word- in these days, because larger amount of based compression methods based on data require to be transferred or backed-up Huffman encoding, LZW or BWT were and capacity of media or speed of network tested. This paper describes word-based lines increase slowly. -
Audio Coding for Digital Broadcasting
Recommendation ITU-R BS.1196-7 (01/2019) Audio coding for digital broadcasting BS Series Broadcasting service (sound) ii Rec. ITU-R BS.1196-7 Foreword The role of the Radiocommunication Sector is to ensure the rational, equitable, efficient and economical use of the radio- frequency spectrum by all radiocommunication services, including satellite services, and carry out studies without limit of frequency range on the basis of which Recommendations are adopted. The regulatory and policy functions of the Radiocommunication Sector are performed by World and Regional Radiocommunication Conferences and Radiocommunication Assemblies supported by Study Groups. Policy on Intellectual Property Right (IPR) ITU-R policy on IPR is described in the Common Patent Policy for ITU-T/ITU-R/ISO/IEC referenced in Resolution ITU-R 1. Forms to be used for the submission of patent statements and licensing declarations by patent holders are available from http://www.itu.int/ITU-R/go/patents/en where the Guidelines for Implementation of the Common Patent Policy for ITU-T/ITU-R/ISO/IEC and the ITU-R patent information database can also be found. Series of ITU-R Recommendations (Also available online at http://www.itu.int/publ/R-REC/en) Series Title BO Satellite delivery BR Recording for production, archival and play-out; film for television BS Broadcasting service (sound) BT Broadcasting service (television) F Fixed service M Mobile, radiodetermination, amateur and related satellite services P Radiowave propagation RA Radio astronomy RS Remote sensing systems S Fixed-satellite service SA Space applications and meteorology SF Frequency sharing and coordination between fixed-satellite and fixed service systems SM Spectrum management SNG Satellite news gathering TF Time signals and frequency standards emissions V Vocabulary and related subjects Note: This ITU-R Recommendation was approved in English under the procedure detailed in Resolution ITU-R 1. -
The Basic Principles of Data Compression
The Basic Principles of Data Compression Author: Conrad Chung, 2BrightSparks Introduction Internet users who download or upload files from/to the web, or use email to send or receive attachments will most likely have encountered files in compressed format. In this topic we will cover how compression works, the advantages and disadvantages of compression, as well as types of compression. What is Compression? Compression is the process of encoding data more efficiently to achieve a reduction in file size. One type of compression available is referred to as lossless compression. This means the compressed file will be restored exactly to its original state with no loss of data during the decompression process. This is essential to data compression as the file would be corrupted and unusable should data be lost. Another compression category which will not be covered in this article is “lossy” compression often used in multimedia files for music and images and where data is discarded. Lossless compression algorithms use statistic modeling techniques to reduce repetitive information in a file. Some of the methods may include removal of spacing characters, representing a string of repeated characters with a single character or replacing recurring characters with smaller bit sequences. Advantages/Disadvantages of Compression Compression of files offer many advantages. When compressed, the quantity of bits used to store the information is reduced. Files that are smaller in size will result in shorter transmission times when they are transferred on the Internet. Compressed files also take up less storage space. File compression can zip up several small files into a single file for more convenient email transmission. -
A Practical Approach to Spatiotemporal Data Compression
A Practical Approach to Spatiotemporal Data Compres- sion Niall H. Robinson1, Rachel Prudden1 & Alberto Arribas1 1Informatics Lab, Met Office, Exeter, UK. Datasets representing the world around us are becoming ever more unwieldy as data vol- umes grow. This is largely due to increased measurement and modelling resolution, but the problem is often exacerbated when data are stored at spuriously high precisions. In an effort to facilitate analysis of these datasets, computationally intensive calculations are increasingly being performed on specialised remote servers before the reduced data are transferred to the consumer. Due to bandwidth limitations, this often means data are displayed as simple 2D data visualisations, such as scatter plots or images. We present here a novel way to efficiently encode and transmit 4D data fields on-demand so that they can be locally visualised and interrogated. This nascent “4D video” format allows us to more flexibly move the bound- ary between data server and consumer client. However, it has applications beyond purely scientific visualisation, in the transmission of data to virtual and augmented reality. arXiv:1604.03688v2 [cs.MM] 27 Apr 2016 With the rise of high resolution environmental measurements and simulation, extremely large scientific datasets are becoming increasingly ubiquitous. The scientific community is in the pro- cess of learning how to efficiently make use of these unwieldy datasets. Increasingly, people are interacting with this data via relatively thin clients, with data analysis and storage being managed by a remote server. The web browser is emerging as a useful interface which allows intensive 1 operations to be performed on a remote bespoke analysis server, but with the resultant information visualised and interrogated locally on the client1, 2. -
(A/V Codecs) REDCODE RAW (.R3D) ARRIRAW
What is a Codec? Codec is a portmanteau of either "Compressor-Decompressor" or "Coder-Decoder," which describes a device or program capable of performing transformations on a data stream or signal. Codecs encode a stream or signal for transmission, storage or encryption and decode it for viewing or editing. Codecs are often used in videoconferencing and streaming media solutions. A video codec converts analog video signals from a video camera into digital signals for transmission. It then converts the digital signals back to analog for display. An audio codec converts analog audio signals from a microphone into digital signals for transmission. It then converts the digital signals back to analog for playing. The raw encoded form of audio and video data is often called essence, to distinguish it from the metadata information that together make up the information content of the stream and any "wrapper" data that is then added to aid access to or improve the robustness of the stream. Most codecs are lossy, in order to get a reasonably small file size. There are lossless codecs as well, but for most purposes the almost imperceptible increase in quality is not worth the considerable increase in data size. The main exception is if the data will undergo more processing in the future, in which case the repeated lossy encoding would damage the eventual quality too much. Many multimedia data streams need to contain both audio and video data, and often some form of metadata that permits synchronization of the audio and video. Each of these three streams may be handled by different programs, processes, or hardware; but for the multimedia data stream to be useful in stored or transmitted form, they must be encapsulated together in a container format. -
Multimedia Compression Techniques for Streaming
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-12, October 2019 Multimedia Compression Techniques for Streaming Preethal Rao, Krishna Prakasha K, Vasundhara Acharya most of the audio codes like MP3, AAC etc., are lossy as Abstract: With the growing popularity of streaming content, audio files are originally small in size and thus need not have streaming platforms have emerged that offer content in more compression. In lossless technique, the file size will be resolutions of 4k, 2k, HD etc. Some regions of the world face a reduced to the maximum possibility and thus quality might be terrible network reception. Delivering content and a pleasant compromised more when compared to lossless technique. viewing experience to the users of such locations becomes a The popular codecs like MPEG-2, H.264, H.265 etc., make challenge. audio/video streaming at available network speeds is just not feasible for people at those locations. The only way is to use of this. FLAC, ALAC are some audio codecs which use reduce the data footprint of the concerned audio/video without lossy technique for compression of large audio files. The goal compromising the quality. For this purpose, there exists of this paper is to identify existing techniques in audio-video algorithms and techniques that attempt to realize the same. compression for transmission and carry out a comparative Fortunately, the field of compression is an active one when it analysis of the techniques based on certain parameters. The comes to content delivering. With a lot of algorithms in the play, side outcome would be a program that would stream the which one actually delivers content while putting less strain on the audio/video file of our choice while the main outcome is users' network bandwidth? This paper carries out an extensive finding out the compression technique that performs the best analysis of present popular algorithms to come to the conclusion of the best algorithm for streaming data. -
TMS320C6000 U-Law and A-Law Companding with Software Or The
Application Report SPRA634 - April 2000 TMS320C6000t µ-Law and A-Law Companding with Software or the McBSP Mark A. Castellano Digital Signal Processing Solutions Todd Hiers Rebecca Ma ABSTRACT This document describes how to perform data companding with the TMS320C6000 digital signal processors (DSP). Companding refers to the compression and expansion of transfer data before and after transmission, respectively. The multichannel buffered serial port (McBSP) in the TMS320C6000 supports two companding formats: µ-Law and A-Law. Both companding formats are specified in the CCITT G.711 recommendation [1]. This application report discusses how to use the McBSP to perform data companding in both the µ-Law and A-Law formats. This document also discusses how to perform companding of data not coming into the device but instead located in a buffer in processor memory. Sample McBSP setup codes are provided for reference. In addition, this application report discusses µ-Law and A-Law companding in software. The appendix provides software companding codes and a brief discussion on the companding theory. Contents 1 Design Problem . 2 2 Overview . 2 3 Companding With the McBSP. 3 3.1 µ-Law Companding . 3 3.1.1 McBSP Register Configuration for µ-Law Companding. 4 3.2 A-Law Companding. 4 3.2.1 McBSP Register Configuration for A-Law Companding. 4 3.3 Companding Internal Data. 5 3.3.1 Non-DLB Mode. 5 3.3.2 DLB Mode . 6 3.4 Sample C Functions. 6 4 Companding With Software. 6 5 Conclusion . 7 6 References . 7 TMS320C6000 is a trademark of Texas Instruments Incorporated. -
Dictionary Based Compression for Images
INTERNATIONAL JOURNAL OF COMPUTERS Issue 3, Volume 6, 2012 Dictionary Based Compression for Images Bruno Carpentieri Ziv and Lempel in [1]. Abstract—Lempel-Ziv methods were original introduced to By limiting what could enter the dictionary, LZ2 assures compress one-dimensional data (text, object codes, etc.) but recently that there is at most one instance for each possible pattern in they have been successfully used in image compression. the dictionary. Constantinescu and Storer in [6] introduced a single-pass vector Initially the dictionary is empty. The coding pass consists of quantization algorithm that, with no training or previous knowledge of the digital data was able to achieve better compression results with searching the dictionary for the longest entry that is a prefix of respect to the JPEG standard and had also important computational a string starting at the current coding position. advantages. The index of the match is transmitted to the decoder using We review some of our recent work on LZ-based, single pass, log N bits, where N is the current size of the dictionary. adaptive algorithms for the compression of digital images, taking into 2 account the theoretical optimality of these approach, and we A new pattern is introduced into the dictionary by experimentally analyze the behavior of this algorithm with respect to concatenating the current match with the next character that the local dictionary size and with respect to the compression of bi- has to be encoded. level images. The dictionary of LZ2 continues to grow throughout the coding process. Keywords—Image compression, textual substitution methods. -
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). -
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.