Video Coding Standards H.261 & H.263
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Free Lossless Image Format
FREE LOSSLESS IMAGE FORMAT Jon Sneyers and Pieter Wuille [email protected] [email protected] Cloudinary Blockstream ICIP 2016, September 26th DON’T WE HAVE ENOUGH IMAGE FORMATS ALREADY? • JPEG, PNG, GIF, WebP, JPEG 2000, JPEG XR, JPEG-LS, JBIG(2), APNG, MNG, BPG, TIFF, BMP, TGA, PCX, PBM/PGM/PPM, PAM, … • Obligatory XKCD comic: YES, BUT… • There are many kinds of images: photographs, medical images, diagrams, plots, maps, line art, paintings, comics, logos, game graphics, textures, rendered scenes, scanned documents, screenshots, … EVERYTHING SUCKS AT SOMETHING • None of the existing formats works well on all kinds of images. • JPEG / JP2 / JXR is great for photographs, but… • PNG / GIF is great for line art, but… • WebP: basically two totally different formats • Lossy WebP: somewhat better than (moz)JPEG • Lossless WebP: somewhat better than PNG • They are both .webp, but you still have to pick the format GOAL: ONE FORMAT THAT COMPRESSES ALL IMAGES WELL EXPERIMENTAL RESULTS Corpus Lossless formats JPEG* (bit depth) FLIF FLIF* WebP BPG PNG PNG* JP2* JXR JLS 100% 90% interlaced PNGs, we used OptiPNG [21]. For BPG we used [4] 8 1.002 1.000 1.234 1.318 1.480 2.108 1.253 1.676 1.242 1.054 0.302 the options -m 9 -e jctvc; for WebP we used -m 6 -q [4] 16 1.017 1.000 / / 1.414 1.502 1.012 2.011 1.111 / / 100. For the other formats we used default lossless options. [5] 8 1.032 1.000 1.099 1.163 1.429 1.664 1.097 1.248 1.500 1.017 0.302� [6] 8 1.003 1.000 1.040 1.081 1.282 1.441 1.074 1.168 1.225 0.980 0.263 Figure 4 shows the results; see [22] for more details. -
Video Codec Requirements and Evaluation Methodology
Video Codec Requirements 47pt 30pt and Evaluation Methodology Color::white : LT Medium Font to be used by customers and : Arial www.huawei.com draft-filippov-netvc-requirements-01 Alexey Filippov, Huawei Technologies 35pt Contents Font to be used by customers and partners : • An overview of applications • Requirements 18pt • Evaluation methodology Font to be used by customers • Conclusions and partners : Slide 2 Page 2 35pt Applications Font to be used by customers and partners : • Internet Protocol Television (IPTV) • Video conferencing 18pt • Video sharing Font to be used by customers • Screencasting and partners : • Game streaming • Video monitoring / surveillance Slide 3 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : • Basic requirements: . Random access to pictures 18pt Random Access Period (RAP) should be kept small enough (approximately, 1-15 seconds); Font to be used by customers . Temporal (frame-rate) scalability; and partners : . Error robustness • Optional requirements: . resolution and quality (SNR) scalability Slide 4 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : Resolution Frame-rate, fps Picture access mode 2160p (4K),3840x2160 60 RA 18pt 1080p, 1920x1080 24, 50, 60 RA 1080i, 1920x1080 30 (60 fields per second) RA Font to be used by customers and partners : 720p, 1280x720 50, 60 RA 576p (EDTV), 720x576 25, 50 RA 576i (SDTV), 720x576 25, 30 RA 480p (EDTV), 720x480 50, 60 RA 480i (SDTV), 720x480 25, 30 RA Slide 5 35pt Video conferencing Font to be used by customers and partners : • Basic requirements: . Delay should be kept as low as possible 18pt The preferable and maximum delay values should be less than 100 ms and 350 ms, respectively Font to be used by customers . -
Quadtree Based JBIG Compression
Quadtree Based JBIG Compression B. Fowler R. Arps A. El Gamal D. Yang ISL, Stanford University, Stanford, CA 94305-4055 ffowler,arps,abbas,[email protected] Abstract A JBIG compliant, quadtree based, lossless image compression algorithm is describ ed. In terms of the numb er of arithmetic co ding op erations required to co de an image, this algorithm is signi cantly faster than previous JBIG algorithm variations. Based on this criterion, our algorithm achieves an average sp eed increase of more than 9 times with only a 5 decrease in compression when tested on the eight CCITT bi-level test images and compared against the basic non-progressive JBIG algorithm. The fastest JBIG variation that we know of, using \PRES" resolution reduction and progressive buildup, achieved an average sp eed increase of less than 6 times with a 7 decrease in compression, under the same conditions. 1 Intro duction In facsimile applications it is desirable to integrate a bilevel image sensor with loss- less compression on the same chip. Suchintegration would lower p ower consumption, improve reliability, and reduce system cost. To reap these b ene ts, however, the se- lection of the compression algorithm must takeinto consideration the implementation tradeo s intro duced byintegration. On the one hand, integration enhances the p os- sibility of parallelism which, if prop erly exploited, can sp eed up compression. On the other hand, the compression circuitry cannot b e to o complex b ecause of limitations on the available chip area. Moreover, most of the chip area on a bilevel image sensor must b e o ccupied by photo detectors, leaving only the edges for digital logic. -
(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. -
MPEG Compression Is Based on Processing 8 X 8 Pixel Blocks
MPEG-1 & MPEG-2 Compression 6th of March, 2002, Mauri Kangas Contents: • MPEG Background • MPEG-1 Standard (shortly) • MPEG-2 Standard (shortly) • MPEG-1 and MPEG-2 Differencies • MPEG Encoding and Decoding • Colour sub-sampling • Motion Compensation • Slices, macroblocks, blocks, etc. • DCT • MPEG-2 bit stream syntax • MPEG-2 • Supported Features • Levels • Profiles • DVB Systems 1 © Mauri Kangas 2002 MPEG-1,2.PPT/ 24.02.2002 / Mauri Kangas MPEG Background • MPEG = Motion Picture Expert Group • ISO/IEC JTC1/SC29 • WG11 Motion Picture Experts Group (MPEG) • WG10 Joint Photographic Experts Group (JPEG) • WG7 Computer Graphics Experts Group (CGEG) • WG9 Joint Bi-level Image coding experts Group (JBIG) • WG12 Multimedia and Hypermedia information coding Experts Group (MHEG) • MPEG-1,2 Standardization 1988- • Requirement • System • Video • Audio • Implementation • Testing • Latest MPEG Standardization: MPEG-4, MPEG-7, MPEG-21 2 © Mauri Kangas 2002 MPEG-1,2.PPT/ 24.02.2002 / Mauri Kangas MPEG-1 Standard ISO/IEC 11172-2 (1991) "Coding of moving pictures and associated audio for digital storage media" Video • optimized for bitrates around 1.5 Mbit/s • originally optimized for SIF picture format, but not limited to it: • 352x240 pixels a 30 frames/sec [ NTSC based ] • 352x288 pixels at 25 frames/sec [ PAL based ] • progressive frames only - no direct provision for interlaced video applications, such as broadcast television Audio • joint stereo audio coding at 192 kbit/s (layer 2) System • mainly designed for error-free digital storage media • -
CT8021 H.32X G.723.1/G.728 Truespeech Co-Processor
CT8021 H.32x G.723.1/G.728 TrueSpeech Co-Processor Introduction Features The CT8021 is a speech co-processor which · TrueSpeechâ G.723.1 at 6.3, 5.3, 4.8 and performs full duplex speech compression and de- 4.1 kbps at 8KHz sampling rate (including compression functions. It provides speech G.723.1 Annex A VAD/CNG) compression for H.320, H.323 and H.324 · G.728 16 Kbps LD-CELP Multimedia Visual Telephony / Video · Download of additional speech compression Conferencing products and DSVD Modems. The software modules into external sram for CT8021 has built-in TrueSpeechâ G.723.1 (for TrueSpeechâ 8.5, G.722 & G.729-A/B H.323 and H.324) as well as G.728 LD-CELP · Real-time Full duplex or Half duplex speech speech compression (for H.320). This combination compression and decompression of ITU speech compression standards within a · Acoustic Echo Cancellation concurrent with single device enables the creation of a single full-duplex speech compression multimedia terminal which can operate in all · Full Duplex standalone Speakerphone types of Video Conferencing systems including · Host-to-Host (codec-less) and Host-CODEC H.320 ISDN-based, H.324 POTS-based, and modes of operation H.323 LAN/Internet-based. TrueSpeechâ G.723.1 · Parallel 8-bit host interface provides simple provides compressed data rates of 6.3 and 5.3 memory-mapped I/O host connection. Kbps and includes G.723.1 Annex A VAD/CNG · 1 or 2-channel DMA support (Single Cycle “silence” compression which can supply an even and Burst Modes) lower average bit rate. -
Netflix and the Development of the Internet Television Network
Syracuse University SURFACE Dissertations - ALL SURFACE May 2016 Netflix and the Development of the Internet Television Network Laura Osur Syracuse University Follow this and additional works at: https://surface.syr.edu/etd Part of the Social and Behavioral Sciences Commons Recommended Citation Osur, Laura, "Netflix and the Development of the Internet Television Network" (2016). Dissertations - ALL. 448. https://surface.syr.edu/etd/448 This Dissertation is brought to you for free and open access by the SURFACE at SURFACE. It has been accepted for inclusion in Dissertations - ALL by an authorized administrator of SURFACE. For more information, please contact [email protected]. Abstract When Netflix launched in April 1998, Internet video was in its infancy. Eighteen years later, Netflix has developed into the first truly global Internet TV network. Many books have been written about the five broadcast networks – NBC, CBS, ABC, Fox, and the CW – and many about the major cable networks – HBO, CNN, MTV, Nickelodeon, just to name a few – and this is the fitting time to undertake a detailed analysis of how Netflix, as the preeminent Internet TV networks, has come to be. This book, then, combines historical, industrial, and textual analysis to investigate, contextualize, and historicize Netflix's development as an Internet TV network. The book is split into four chapters. The first explores the ways in which Netflix's development during its early years a DVD-by-mail company – 1998-2007, a period I am calling "Netflix as Rental Company" – lay the foundations for the company's future iterations and successes. During this period, Netflix adapted DVD distribution to the Internet, revolutionizing the way viewers receive, watch, and choose content, and built a brand reputation on consumer-centric innovation. -
TR 101 329-7 V1.1.1 (2000-11) Technical Report
ETSI TR 101 329-7 V1.1.1 (2000-11) Technical Report TIPHON; Design Guide; Part 7: Design Guide for Elements of a TIPHON connection from an end-to-end speech transmission performance point of view 2 ETSI TR 101 329-7 V1.1.1 (2000-11) Reference DTR/TIPHON-05011 Keywords internet, IP, network, performance, protocol, quality, speech, voice ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.:+33492944200 Fax:+33493654716 Siret N° 348 623 562 00017 - NAF 742 C Association à but non lucratif enregistrée à la Sous-Préfecture de Grasse (06) N° 7803/88 Important notice Individual copies of the present document can be downloaded from: http://www.etsi.org The present document may be made available in more than one electronic version or in print. In any case of existing or perceived difference in contents between such versions, the reference version is the Portable Document Format (PDF). In case of dispute, the reference shall be the printing on ETSI printers of the PDF version kept on a specific network drive within ETSI Secretariat. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current status of this and other ETSI documents is available at http://www.etsi.org/tb/status/ If you find errors in the present document, send your comment to: [email protected] Copyright Notification No part may be reproduced except as authorized by written permission. The copyright and the foregoing restriction extend to reproduction in all media. -
The H.264 Advanced Video Coding (AVC) Standard
Whitepaper: The H.264 Advanced Video Coding (AVC) Standard What It Means to Web Camera Performance Introduction A new generation of webcams is hitting the market that makes video conferencing a more lifelike experience for users, thanks to adoption of the breakthrough H.264 standard. This white paper explains some of the key benefits of H.264 encoding and why cameras with this technology should be on the shopping list of every business. The Need for Compression Today, Internet connection rates average in the range of a few megabits per second. While VGA video requires 147 megabits per second (Mbps) of data, full high definition (HD) 1080p video requires almost one gigabit per second of data, as illustrated in Table 1. Table 1. Display Resolution Format Comparison Format Horizontal Pixels Vertical Lines Pixels Megabits per second (Mbps) QVGA 320 240 76,800 37 VGA 640 480 307,200 147 720p 1280 720 921,600 442 1080p 1920 1080 2,073,600 995 Video Compression Techniques Digital video streams, especially at high definition (HD) resolution, represent huge amounts of data. In order to achieve real-time HD resolution over typical Internet connection bandwidths, video compression is required. The amount of compression required to transmit 1080p video over a three megabits per second link is 332:1! Video compression techniques use mathematical algorithms to reduce the amount of data needed to transmit or store video. Lossless Compression Lossless compression changes how data is stored without resulting in any loss of information. Zip files are losslessly compressed so that when they are unzipped, the original files are recovered. -
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter Prediction using Binary classification techniques A graduate project submitted in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering by Alex Kit Romero May 2020 The graduate project of Alex Kit Romero is approved: ____________________________________ ____________ Dr. Katya Mkrtchyan Date ____________________________________ ____________ Dr. Kyle Dewey Date ____________________________________ ____________ Dr. John J. Noga, Chair Date California State University, Northridge ii Dedication This project is dedicated to all of the Computer Science professors that I have come in contact with other the years who have inspired and encouraged me to pursue a career in computer science. The words and wisdom of these professors are what pushed me to try harder and accomplish more than I ever thought possible. I would like to give a big thanks to the open source community and my fellow cohort of computer science co-workers for always being there with answers to my numerous questions and inquiries. Without their guidance and expertise, I could not have been successful. Lastly, I would like to thank my friends and family who have supported and uplifted me throughout the years. Thank you for believing in me and always telling me to never give up. iii Table of Contents Signature Page ................................................................................................................................ ii Dedication ..................................................................................................................................... -
A Comparison of Video Formats for Online Teaching Ross A
Contemporary Issues in Education Research – First Quarter 2017 Volume 10, Number 1 A Comparison Of Video Formats For Online Teaching Ross A. Malaga, Montclair State University, USA Nicole B. Koppel, Montclair State University, USA ABSTRACT The use of video to deliver content to students online has become increasingly popular. However, educators are often plagued with the question of which format to use to deliver asynchronous video material. Whether it is a College or University committing to a common video format or an individual instructor selecting the method that works best for his or her course, this research presents a comparison of various video formats that can be applied to online education and provides guidance in which one to select. Keywords: Online Teaching; Video Formats; Technology Acceptance Model INTRODUCTION istance learning is one of the most talked-about topics in higher education today. Online and hybrid (or blended) learning removes location and time-bound constraints of the traditional college classroom to a learning environment that can occur anytime or anywhere in a global environment. DAccording to research by the Online Learning Consortium, over 5 million students took an online course in the Fall 2014 semester. This represents an increase in online enrollment of over 3.9% in just one year. In 2014, 28% of higher education students took one or more courses online (Allen, I. E. and Seaman, J, 2016). With this incredible growth, albeit slower than the growth in previous years, institutions of higher education are continuing to increase their online course and program offerings. As such, institutions need to find easy to develop, easy to use, reliable, and reasonably priced technologies to deliver online content. -
CISC 3635 [36] Multimedia Coding and Compression
Brooklyn College Department of Computer and Information Sciences CISC 3635 [36] Multimedia Coding and Compression 3 hours lecture; 3 credits Media types and their representation. Multimedia coding and compression. Lossless data compression. Lossy data compression. Compression standards: text, audio, image, fax, and video. Course Outline: 1. Media Types and Their Representations (Week 1-2) a. Text b. Analog and Digital Audio c. Digitization of Sound d. Musical Instrument Digital Interface (MIDI) e. Audio Coding: Pulse Code Modulation (PCM) f. Graphics and Images Data Representation g. Image File Formats: GIF, JPEG, PNG, TIFF, BMP, etc. h. Color Science and Models i. Fax j. Video Concepts k. Video Signals: Component, Composite, S-Video l. Analog and Digital Video: NTSC, PAL, SECAM, HDTV 2. Lossless Compression Algorithms (Week 3-4) a. Basics of Information Theory b. Run-Length Coding c. Variable Length Coding: Huffman Coding d. Basic Fax Compression e. Dictionary Based Coding: LZW Compression f. Arithmetic Coding g. Lossless Image Compression 3. Lossy Compression Algorithms (Week 5-6) a. Distortion Measures b. Quantization c. Transform Coding d. Wavelet Based Coding e. Embedded Zerotree of Wavelet (EZW) Coefficients 4. Basic Audio Compression (Week 7-8) a. Differential Coders: DPCM, ADPCM, DM b. Vocoders c. Linear Predictive Coding (LPC) d. CELP e. Audio Standards: G.711, G.726, G.723, G.728. G.729, etc. 5. Image Compression Standards (Week 9) a. The JPEG Standard b. JPEG 2000 c. JPEG-LS d. Bilevel Image Compression Standards: JBIG, JBIG2 6. Basic Video Compression (Week 10) a. Video Compression Based on Motion Compensation b. Search for Motion Vectors c.