An Explanation of Video Compression Techniques. Table of Contents
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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. -
Versatile Video Coding – the Next-Generation Video Standard of the Joint Video Experts Team
31.07.2018 Versatile Video Coding – The Next-Generation Video Standard of the Joint Video Experts Team Mile High Video Workshop, Denver July 31, 2018 Gary J. Sullivan, JVET co-chair Acknowledgement: Presentation prepared with Jens-Rainer Ohm and Mathias Wien, Institute of Communication Engineering, RWTH Aachen University 1. Introduction Versatile Video Coding – The Next-Generation Video Standard of the Joint Video Experts Team 1 31.07.2018 Video coding standardization organisations • ISO/IEC MPEG = “Moving Picture Experts Group” (ISO/IEC JTC 1/SC 29/WG 11 = International Standardization Organization and International Electrotechnical Commission, Joint Technical Committee 1, Subcommittee 29, Working Group 11) • ITU-T VCEG = “Video Coding Experts Group” (ITU-T SG16/Q6 = International Telecommunications Union – Telecommunications Standardization Sector (ITU-T, a United Nations Organization, formerly CCITT), Study Group 16, Working Party 3, Question 6) • JVT = “Joint Video Team” collaborative team of MPEG & VCEG, responsible for developing AVC (discontinued in 2009) • JCT-VC = “Joint Collaborative Team on Video Coding” team of MPEG & VCEG , responsible for developing HEVC (established January 2010) • JVET = “Joint Video Experts Team” responsible for developing VVC (established Oct. 2015) – previously called “Joint Video Exploration Team” 3 Versatile Video Coding – The Next-Generation Video Standard of the Joint Video Experts Team Gary Sullivan | Jens-Rainer Ohm | Mathias Wien | July 31, 2018 History of international video coding standardization -
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs Jan P. Klopp Keng-Chi Liu National Taiwan University Taiwan AI Labs [email protected] [email protected] Liang-Gee Chen Shao-Yi Chien National Taiwan University [email protected] [email protected] Abstract Lossy compression optimises the objective Lossy image compression is often limited by the sim- L = R + λD (1) plicity of the chosen loss measure. Recent research sug- gests that generative adversarial networks have the ability where R and D stand for rate and distortion, respectively, to overcome this limitation and serve as a multi-modal loss, and λ controls their weight relative to each other. In prac- especially for textures. Together with learned image com- tice, computational efficiency is another constraint as at pression, these two techniques can be used to great effect least the decoder needs to process high resolutions in real- when relaxing the commonly employed tight measures of time under a limited power envelope, typically necessitating distortion. However, convolutional neural network-based dedicated hardware implementations. Requirements for the algorithms have a large computational footprint. Ideally, encoder are more relaxed, often allowing even offline en- an existing conventional codec should stay in place, ensur- coding without demanding real-time capability. ing faster adoption and adherence to a balanced computa- Recent research has developed along two lines: evolu- tional envelope. tion of exiting coding technologies, such as H264 [41] or As a possible avenue to this goal, we propose and investi- H265 [35], culminating in the most recent AV1 codec, on gate how learned image coding can be used as a surrogate the one hand. -
Completed Projects / Projets Terminés
Completed Projects / Projets terminés New Standards — New Editions — Special Publications Please note that the following standards were developed by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), and have been adopted by the Canadian Standards Association. These standards are available in PDF format only. CAN/CSA-ISO/IEC 2593:02, 4th edition Information Technology–Telecommunications and Information Exchange Between Systems–34-Pole DTE/DCE Interface Connector Mateability Dimensions and Contact Number Assignments (Adopted ISO/IEC 2593:2000).................................... $85 CAN/CSA-ISO/IEC 7811-2:02, 3rd edition Identification Cards–Recording Technique–Part 2: Magnetic Stripe–Low Coercivity (Adopted ISO/IEC 7811-2:2001) .................................................................................... $95 CAN/CSA-ISO/IEC 8208:02, 4th edition Information Technology–Data Communications–X.25 Packet Layer Protocol for Data Terminal Equipment (Adopted ISO/IEC 8208:2000) ............................................ $220 CAN/CSA-ISO/IEC 8802-3:02, 2nd edition Information Technology–Telecommunications and Information Exchange Between Systems–Local and Metropolitan Area Networks–Specific Requirements–Part 3: Carrier Sense Multiple Access with Collision Detection (CSMA/CD) Access Method and Physical Layer (Adopted ISO/IEC 8802-3:2000/IEEE Std 802.3, 2000) ................. $460 CAN/CSA-ISO/IEC 9798-1:02, 2nd edition Information Technology–Security Techniques–Entity Authentication–Part -
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. -
(L3) - Audio/Picture Coding
Committee: (L3) - Audio/Picture Coding National Designation Title (Click here to purchase standards) ISO/IEC Document L3 INCITS/ISO/IEC 9281-1:1990:[R2013] Information technology - Picture Coding Methods - Part 1: Identification IS 9281-1:1990 INCITS/ISO/IEC 9281-2:1990:[R2013] Information technology - Picture Coding Methods - Part 2: Procedure for Registration IS 9281-2:1990 INCITS/ISO/IEC 9282-1:1988:[R2013] Information technology - Coded Representation of Computer Graphics Images - Part IS 9282-1:1988 1: Encoding principles for picture representation in a 7-bit or 8-bit environment :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 7: IS 13522-7:2001 Interoperability and conformance testing for ISO/IEC 13522-5 (MHEG-7) :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 5: IS 13522-5:1997 Support for Base-Level Interactive Applications (MHEG-5) :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 3: IS 13522-3:1997 MHEG script interchange representation (MHEG-3) :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 6: IS 13522-6:1998 Support for enhanced interactive applications (MHEG-6) :[] Information technology - Coding of Multimedia and Hypermedia Information - Part 8: IS 13522-8:2001 XML notation for ISO/IEC 13522-5 (MHEG-8) Created: 11/16/2014 Page 1 of 44 Committee: (L3) - Audio/Picture Coding National Designation Title (Click here to purchase standards) ISO/IEC Document :[] Information technology - Coding -
Chapter 9 Image Compression Standards
Fundamentals of Multimedia, Chapter 9 Chapter 9 Image Compression Standards 9.1 The JPEG Standard 9.2 The JPEG2000 Standard 9.3 The JPEG-LS Standard 9.4 Bi-level Image Compression Standards 9.5 Further Exploration 1 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 9.1 The JPEG Standard JPEG is an image compression standard that was developed • by the “Joint Photographic Experts Group”. JPEG was for- mally accepted as an international standard in 1992. JPEG is a lossy image compression method. It employs a • transform coding method using the DCT (Discrete Cosine Transform). An image is a function of i and j (or conventionally x and y) • in the spatial domain. The 2D DCT is used as one step in JPEG in order to yield a frequency response which is a function F (u, v) in the spatial frequency domain, indexed by two integers u and v. 2 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 Observations for JPEG Image Compression The effectiveness of the DCT transform coding method in • JPEG relies on 3 major observations: Observation 1: Useful image contents change relatively slowly across the image, i.e., it is unusual for intensity values to vary widely several times in a small area, for example, within an 8 8 × image block. much of the information in an image is repeated, hence “spa- • tial redundancy”. 3 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 Observations for JPEG Image Compression (cont’d) Observation 2: Psychophysical experiments suggest that hu- mans are much less likely to notice the loss of very high spatial frequency components than the loss of lower frequency compo- nents. -
(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. -
Image Compression Using Discrete Cosine Transform Method
Qusay Kanaan Kadhim, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.9, September- 2016, pg. 186-192 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 5.258 IJCSMC, Vol. 5, Issue. 9, September 2016, pg.186 – 192 Image Compression Using Discrete Cosine Transform Method Qusay Kanaan Kadhim Al-Yarmook University College / Computer Science Department, Iraq [email protected] ABSTRACT: The processing of digital images took a wide importance in the knowledge field in the last decades ago due to the rapid development in the communication techniques and the need to find and develop methods assist in enhancing and exploiting the image information. The field of digital images compression becomes an important field of digital images processing fields due to the need to exploit the available storage space as much as possible and reduce the time required to transmit the image. Baseline JPEG Standard technique is used in compression of images with 8-bit color depth. Basically, this scheme consists of seven operations which are the sampling, the partitioning, the transform, the quantization, the entropy coding and Huffman coding. First, the sampling process is used to reduce the size of the image and the number bits required to represent it. Next, the partitioning process is applied to the image to get (8×8) image block. Then, the discrete cosine transform is used to transform the image block data from spatial domain to frequency domain to make the data easy to process. -
Task-Aware Quantization Network for JPEG Image Compression
Task-Aware Quantization Network for JPEG Image Compression Jinyoung Choi1 and Bohyung Han1 Dept. of ECE & ASRI, Seoul National University, Korea fjin0.choi,[email protected] Abstract. We propose to learn a deep neural network for JPEG im- age compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective func- tion of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approx- imates bitrates using simple network blocks|two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses| two for semantic image understanding and another two for conventional image compression|and demonstrate the effectiveness of our approach to the individual tasks. Keywords: JPEG image compression, adaptive quantization, bitrate approximation. 1 Introduction Image compression is a classical task to reduce the file size of an input image while minimizing the loss of visual quality. This task has two categories|lossy and lossless compression. Lossless compression algorithms preserve the contents of input images perfectly even after compression, but their compression rates are typically low. On the other hand, lossy compression techniques allow the degra- dation of the original images by quantization and reduce the file size significantly compared to lossless counterparts. -
Security Solutions Y in MPEG Family of MPEG Family of Standards
1 Security solutions in MPEG family of standards TdjEbhiiTouradj Ebrahimi [email protected] NET working? Broadcast Networks and their security 16-17 June 2004, Geneva, Switzerland MPEG: Moving Picture Experts Group 2 • MPEG-1 (1992): MP3, Video CD, first generation set-top box, … • MPEG-2 (1994): Digital TV, HDTV, DVD, DVB, Professional, … • MPEG-4 (1998, 99, ongoing): Coding of Audiovisual Objects • MPEG-7 (2001, ongo ing ): DitiDescription of Multimedia Content • MPEG-21 (2002, ongoing): Multimedia Framework NET working? Broadcast Networks and their security 16-17 June 2004, Geneva, Switzerland MPEG-1 - ISO/IEC 11172:1992 3 • Coding of moving pictures and associated audio for digital storage media at up to about 1,5 Mbit/s – Part 1 Systems - Program Stream – Part 2 Video – Part 3 Audio – Part 4 Conformance – Part 5 Reference software NET working? Broadcast Networks and their security 16-17 June 2004, Geneva, Switzerland MPEG-2 - ISO/IEC 13818:1994 4 • Generic coding of moving pictures and associated audio – Part 1 Systems - joint with ITU – Part 2 Video - joint with ITU – Part 3 Audio – Part 4 Conformance – Part 5 Reference software – Part 6 DSM CC – Par t 7 AAC - Advance d Au dio Co ding – Part 9 RTI - Real Time Interface – Part 10 Conformance extension - DSM-CC – Part 11 IPMP on MPEG-2 Systems NET working? Broadcast Networks and their security 16-17 June 2004, Geneva, Switzerland MPEG-4 - ISO/IEC 14496:1998 5 • Coding of audio-visual objects – Part 1 Systems – Part 2 Visual – Part 3 Audio – Part 4 Conformance – Part 5 Reference -
JPEG and JPEG 2000
JPEG and JPEG 2000 Past, present, and future Richard Clark Elysium Ltd, Crowborough, UK [email protected] Planned presentation Brief introduction JPEG – 25 years of standards… Shortfalls and issues Why JPEG 2000? JPEG 2000 – imaging architecture JPEG 2000 – what it is (should be!) Current activities New and continuing work… +44 1892 667411 - [email protected] Introductions Richard Clark – Working in technical standardisation since early 70’s – Fax, email, character coding (8859-1 is basis of HTML), image coding, multimedia – Elysium, set up in ’91 as SME innovator on the Web – Currently looks after JPEG web site, historical archive, some PR, some standards as editor (extensions to JPEG, JPEG-LS, MIME type RFC and software reference for JPEG 2000), HD Photo in JPEG, and the UK MPEG and JPEG committees – Plus some work that is actually funded……. +44 1892 667411 - [email protected] Elysium in Europe ACTS project – SPEAR – advanced JPEG tools ESPRIT project – Eurostill – consensus building on JPEG 2000 IST – Migrator 2000 – tool migration and feature exploitation of JPEG 2000 – 2KAN – JPEG 2000 advanced networking Plus some other involvement through CEN in cultural heritage and medical imaging, Interreg and others +44 1892 667411 - [email protected] 25 years of standards JPEG – Joint Photographic Experts Group, joint venture between ISO and CCITT (now ITU-T) Evolved from photo-videotex, character coding First meeting March 83 – JPEG proper started in July 86. 42nd meeting in Lausanne, next week… Attendance through national