Comparison of Video Compression Standards
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FCC-06-11A1.Pdf
Federal Communications Commission FCC 06-11 Before the FEDERAL COMMUNICATIONS COMMISSION WASHINGTON, D.C. 20554 In the Matter of ) ) Annual Assessment of the Status of Competition ) MB Docket No. 05-255 in the Market for the Delivery of Video ) Programming ) TWELFTH ANNUAL REPORT Adopted: February 10, 2006 Released: March 3, 2006 Comment Date: April 3, 2006 Reply Comment Date: April 18, 2006 By the Commission: Chairman Martin, Commissioners Copps, Adelstein, and Tate issuing separate statements. TABLE OF CONTENTS Heading Paragraph # I. INTRODUCTION.................................................................................................................................. 1 A. Scope of this Report......................................................................................................................... 2 B. Summary.......................................................................................................................................... 4 1. The Current State of Competition: 2005 ................................................................................... 4 2. General Findings ....................................................................................................................... 6 3. Specific Findings....................................................................................................................... 8 II. COMPETITORS IN THE MARKET FOR THE DELIVERY OF VIDEO PROGRAMMING ......... 27 A. Cable Television Service .............................................................................................................. -
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. -
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 . -
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. -
Max Sound V. Google
LAW OFFIClS 0~ _,.._.. \'XIALKUP, MELODIA, KELLY & SCHOEN I"ScRGEt<. 2 A PIKlll SSIONAL CORPORA l ION ..... G!JO CALIFORNIA STRE ET, 2611 1~L OO R -' SA N FRANCISCO, CALIFORNIA 94108-2615 (41 5) 901 7210 4 MICHAEL A. KELLY (State Bar #71460) 5 [email protected] MATTHEW D. DAVIS (State Bar #141986) 6 [email protected] KHALDOUN A. BAGHDADI (State Bar #1901 11 ) 7 kbaghdad i@wal kuplawofficc .com 8 JAY W. EISEN HOFER (pro hac vice to be submitted) GEOFFREY C. JARVIS (pro hac vice to be submitted) 9 ADAM J. LEVJTT ,(pro hac vic~ to be submitted) CATHERINE 0 SUILLEABHAfN (pro hac vice to be submitted) 10 GRANT & EISENHOFER P.A. 30 North LaSall e Street, Suite 1200 ll Chicago, Illinois 60602 Tel: (312) 214-0000 12 CHRJSTOPHER M. JOE (pro hac vice to be submitted) 13 ERJC W. BlJETHER (pro hac vice to be submitted) BRIAN A. CARPENTER (State Bar #262349) 14 MARK A. PERANTI E (pro hac vice to be submitted) BUETHER JOE & CARPENTER, LLC 1.5 1700 Pacific, Suite 4750 Dallas, Texas 75201 16 Tel: (214) 466-1272 ATTORNEYS FOR PLAINTIFFS 17 18 TN THE SUPERIOR COURT OF THE STATE OF CALIFORNIA 19 COUNTY OF SANTA CLARA 20 21 MAX SOUND CORPORATION, VSL CaseNo. 114CVI89231 COMMUNICATIONS LTD .. and VEDANTI 22 SYSTEMS LIMITED, UNLIMITED .JURISDICTION 23 Plainti ITs , COMPLAINT FOR DAMAGES AND INJUNCTIVE RELIEF: 24 V. 1. MISAPPROPRIATION OF TRADE SECRETS 25 GOOGLE, INC. , YOUTUBE, LLC , ON2 2. BREACH OF CONTRACT TECHNOLOGIES, IN C., and DOES 1-100, 3. UNFAIR COMPETITION ~ 26 4. -
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 ..................................................................................................................................... -
NY Amended Class Action Complaint (2009)
SUPREME COURT OF THE STATE OF NEW YORK COUNTY OF QUEENS : COMMERCIAL DIVISION x MICHAEL JIANNARAS, on Behalf of : Index No. 21262/09 Himself and All Others Similarly Situated, : : Plaintiff, : The Honorable Marguerite A. Grays, J.S.C. : vs. : : MIKE ALFANT, MIKE KOPETSKI, J. AMENDED CLASS ACTION COMPLAINT : ALLEN KOSOWSKY, JAMES MEYER, : AFSANEH NAIMOLLAH, THOMAS : WEIGMAN, ON2 TECHNOLOGIES, INC. : and GOOGLE INC., : : Defendants. : x Plaintiff, by his attorneys, alleges upon information and belief, except for those allegations that pertain to him, which are alleged upon personal knowledge, as follows: NATURE OF THE ACTION 1. Plaintiff brings this shareholder class action on behalf of himself and all other public shareholders of On2 Technologies, Inc. (“On2” or the “Company”), against On2 and its Board of Directors (the “Board” or “Individual Defendants”), arising out of the proposed sale of On2 to defendant Google Inc. (“Google”) in a transaction valued at approximately $106.5 million pursuant to which each share of On2 common stock will be exchanged for 60 cents worth of Google Class A common stock (the “Proposed Transaction”). 2. In connection with the Proposed Transaction, however, the Board failed to discharge its fiduciary duties to the shareholders by, inter alia : (i) failing to ensure that they will receive maximum value for their shares; (ii) failing to conduct an appropriate sale process; (iii) implementing preclusive deal protections that will inhibit an alternate transaction; (iv) favoring the interests of certain “insider” shareholders over the interests of the Company’s unaffiliated public shareholders; (v) falsely portraying the Proposed Transaction as one in which the On2 shareholders will receive Google stock in exchange for their shares; and (vi) favoring its own interests in connection with the Proposed Transaction by attempting to extinguish shareholder derivative standing to evade liability for admitted accounting improprieties that resulted in the generation of false financial statements. -
Why Compression Matters
Computational thinking for digital technologies: Snapshot 2 PROGRESS OUTCOME 6 Why compression matters Context Sarah has been investigating the concept of compression coding, by looking at the different ways images can be compressed and the trade-offs of using different methods of compression in relation to the size and quality of images. She researches the topic by doing several web searches and produces a report with her findings. Insight 1: Reasons for compressing files I realised that data compression is extremely useful as it reduces the amount of space needed to store files. Computers and storage devices have limited space, so using smaller files allows you to store more files. Compressed files also download more quickly, which saves time – and money, because you pay less for electricity and bandwidth. Compression is also important in terms of HCI (human-computer interaction), because if images or videos are not compressed they take longer to download and, rather than waiting, many users will move on to another website. Insight 2: Representing images in an uncompressed form Most people don’t realise that a computer image is made up of tiny dots of colour, called pixels (short for “picture elements”). Each pixel has components of red, green and blue light and is represented by a binary number. The more bits used to represent each component, the greater the depth of colour in your image. For example, if red is represented by 8 bits, green by 8 bits and blue by 8 bits, 16,777,216 different colours can be represented, as shown below: 8 bits corresponds to 256 possible numbers in binary red x green x blue = 256 x 256 x 256 = 16,777,216 possible colour combinations 1 Insight 3: Lossy compression and human perception There are many different formats for file compression such as JPEG (image compression), MP3 (audio compression) and MPEG (audio and video compression). -
Video Compression and Communications: from Basics to H.261, H.263, H.264, MPEG2, MPEG4 for DVB and HSDPA-Style Adaptive Turbo-Transceivers
Video Compression and Communications: From Basics to H.261, H.263, H.264, MPEG2, MPEG4 for DVB and HSDPA-Style Adaptive Turbo-Transceivers by c L. Hanzo, P.J. Cherriman, J. Streit Department of Electronics and Computer Science, University of Southampton, UK About the Authors Lajos Hanzo (http://www-mobile.ecs.soton.ac.uk) FREng, FIEEE, FIET, DSc received his degree in electronics in 1976 and his doctorate in 1983. During his 30-year career in telecommunications he has held various research and academic posts in Hungary, Germany and the UK. Since 1986 he has been with the School of Electronics and Computer Science, University of Southampton, UK, where he holds the chair in telecom- munications. He has co-authored 15 books on mobile radio communica- tions totalling in excess of 10 000, published about 700 research papers, acted as TPC Chair of IEEE conferences, presented keynote lectures and been awarded a number of distinctions. Currently he is directing an academic research team, working on a range of research projects in the field of wireless multimedia communications sponsored by industry, the Engineering and Physical Sciences Research Council (EPSRC) UK, the European IST Programme and the Mobile Virtual Centre of Excellence (VCE), UK. He is an enthusiastic supporter of industrial and academic liaison and he offers a range of industrial courses. He is also an IEEE Distinguished Lecturer of both the Communications Society and the Vehicular Technology Society (VTS). Since 2005 he has been a Governer of the VTS. For further information on research in progress and associated publications please refer to http://www-mobile.ecs.soton.ac.uk Peter J.