H.264 and MPEG-4 Video Compression
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The Microsoft Office Open XML Formats New File Formats for “Office 12”
The Microsoft Office Open XML Formats New File Formats for “Office 12” White Paper Published: June 2005 For the latest information, please see http://www.microsoft.com/office/wave12 Contents Introduction ...............................................................................................................................1 From .doc to .docx: a brief history of the Office file formats.................................................1 Benefits of the Microsoft Office Open XML Formats ................................................................2 Integration with Business Data .............................................................................................2 Openness and Transparency ...............................................................................................4 Robustness...........................................................................................................................7 Description of the Microsoft Office Open XML Format .............................................................9 Document Parts....................................................................................................................9 Microsoft Office Open XML Format specifications ...............................................................9 Compatibility with new file formats........................................................................................9 For more information ..............................................................................................................10 -
Why ODF?” - the Importance of Opendocument Format for Governments
“Why ODF?” - The Importance of OpenDocument Format for Governments Documents are the life blood of modern governments and their citizens. Governments use documents to capture knowledge, store critical information, coordinate activities, measure results, and communicate across departments and with businesses and citizens. Increasingly documents are moving from paper to electronic form. To adapt to ever-changing technology and business processes, governments need assurance that they can access, retrieve and use critical records, now and in the future. OpenDocument Format (ODF) addresses these issues by standardizing file formats to give governments true control over their documents. Governments using applications that support ODF gain increased efficiencies, more flexibility and greater technology choice, leading to enhanced capability to communicate with and serve the public. ODF is the ISO Approved International Open Standard for File Formats ODF is the only open standard for office applications, and it is completely vendor neutral. Developed through a transparent, multi-vendor/multi-stakeholder process at OASIS (Organization for the Advancement of Structured Information Standards), it is an open, XML- based document file format for displaying, storing and editing office documents, such as spreadsheets, charts, and presentations. It is available for implementation and use free from any licensing, royalty payments, or other restrictions. In May 2006, it was approved unanimously as an International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) standard. Governments and Businesses are Embracing ODF The promotion and usage of ODF is growing rapidly, demonstrating the global need for control and choice in document applications. For example, many enlightened governments across the globe are making policy decisions to move to ODF. -
Kulkarni Uta 2502M 11649.Pdf
IMPLEMENTATION OF A FAST INTER-PREDICTION MODE DECISION IN H.264/AVC VIDEO ENCODER by AMRUTA KIRAN KULKARNI Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN ELECTRICAL ENGINEERING THE UNIVERSITY OF TEXAS AT ARLINGTON May 2012 ACKNOWLEDGEMENTS First and foremost, I would like to take this opportunity to offer my gratitude to my supervisor, Dr. K.R. Rao, who invested his precious time in me and has been a constant support throughout my thesis with his patience and profound knowledge. His motivation and enthusiasm helped me in all the time of research and writing of this thesis. His advising and mentoring have helped me complete my thesis. Besides my advisor, I would like to thank the rest of my thesis committee. I am also very grateful to Dr. Dongil Han for his continuous technical advice and financial support. I would like to acknowledge my research group partner, Santosh Kumar Muniyappa, for all the valuable discussions that we had together. It helped me in building confidence and motivated towards completing the thesis. Also, I thank all other lab mates and friends who helped me get through two years of graduate school. Finally, my sincere gratitude and love go to my family. They have been my role model and have always showed me right way. Last but not the least; I would like to thank my husband Amey Mahajan for his emotional and moral support. April 20, 2012 ii ABSTRACT IMPLEMENTATION OF A FAST INTER-PREDICTION MODE DECISION IN H.264/AVC VIDEO ENCODER Amruta Kulkarni, M.S The University of Texas at Arlington, 2011 Supervising Professor: K.R. -
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. -
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 -
1. in the New Document Dialog Box, on the General Tab, for Type, Choose Flash Document, Then Click______
1. In the New Document dialog box, on the General tab, for type, choose Flash Document, then click____________. 1. Tab 2. Ok 3. Delete 4. Save 2. Specify the export ________ for classes in the movie. 1. Frame 2. Class 3. Loading 4. Main 3. To help manage the files in a large application, flash MX professional 2004 supports the concept of _________. 1. Files 2. Projects 3. Flash 4. Player 4. In the AppName directory, create a subdirectory named_______. 1. Source 2. Flash documents 3. Source code 4. Classes 5. In Flash, most applications are _________ and include __________ user interfaces. 1. Visual, Graphical 2. Visual, Flash 3. Graphical, Flash 4. Visual, AppName 6. Test locally by opening ________ in your web browser. 1. AppName.fla 2. AppName.html 3. AppName.swf 4. AppName 7. The AppName directory will contain everything in our project, including _________ and _____________ . 1. Source code, Final input 2. Input, Output 3. Source code, Final output 4. Source code, everything 8. In the AppName directory, create a subdirectory named_______. 1. Compiled application 2. Deploy 3. Final output 4. Source code 9. Every Flash application must include at least one ______________. 1. Flash document 2. AppName 3. Deploy 4. Source 10. In the AppName/Source directory, create a subdirectory named __________. 1. Source 2. Com 3. Some domain 4. AppName 11. In the AppName/Source/Com directory, create a sub directory named ______ 1. Some domain 2. Com 3. AppName 4. Source 12. A project is group of related _________ that can be managed via the project panel in the flash. -
Exploring Weak Scalability for FEM Calculations on a GPU-Enhanced Cluster
Exploring weak scalability for FEM calculations on a GPU-enhanced cluster Dominik G¨oddeke a,∗,1, Robert Strzodka b,2, Jamaludin Mohd-Yusof c, Patrick McCormick c,3, Sven H.M. Buijssen a, Matthias Grajewski a and Stefan Turek a aInstitute of Applied Mathematics, University of Dortmund bStanford University, Max Planck Center cComputer, Computational and Statistical Sciences Division, Los Alamos National Laboratory Abstract The first part of this paper surveys co-processor approaches for commodity based clusters in general, not only with respect to raw performance, but also in view of their system integration and power consumption. We then extend previous work on a small GPU cluster by exploring the heterogeneous hardware approach for a large-scale system with up to 160 nodes. Starting with a conventional commodity based cluster we leverage the high bandwidth of graphics processing units (GPUs) to increase the overall system bandwidth that is the decisive performance factor in this scenario. Thus, even the addition of low-end, out of date GPUs leads to improvements in both performance- and power-related metrics. Key words: graphics processors, heterogeneous computing, parallel multigrid solvers, commodity based clusters, Finite Elements PACS: 02.70.-c (Computational Techniques (Mathematics)), 02.70.Dc (Finite Element Analysis), 07.05.Bx (Computer Hardware and Languages), 89.20.Ff (Computer Science and Technology) ∗ Corresponding author. Address: Vogelpothsweg 87, 44227 Dortmund, Germany. Email: [email protected], phone: (+49) 231 755-7218, fax: -5933 1 Supported by the German Science Foundation (DFG), project TU102/22-1 2 Supported by a Max Planck Center for Visual Computing and Communication fellowship 3 Partially supported by the U.S. -
Video Coding Standards
Video coding standards Video signals represent sequences of images or frames which can be transmitted with a rate from 15 to 60 frames per second (fps), that provides the illusion of motion in the displayed signal. Unlike images they contain the so-called temporal redundancy. Temporal redundancy arises from repeated objects in consecutive frames of the video sequence. Such objects can remain, they can move horizontally, vertically, or any combination of directions (translation movement), they can fade in and out, and they can disappear from the image as they move out of view. Temporal redundancy Motion compensation A motion compensation technique is used to compensate the temporal redundancy of video sequence. The main idea of the this method is to predict the displacement of group of pixels (usually block of pixels) from their position in the previous frame. Information about this displacement is represented by motion vectors which are transmitted together with the DCT coded difference between the predicted and the original images. Motion compensation Image VLC DCT Quantizer Buffer - Dequantizer Coded DCT coefficients. IDCT Motion compensated predictor Motion Motion vectors estimation Decoded VL image Buffer Dequantizer IDCT decoder Motion compensated predictor Motion vectors Motion compensation Previous frame Current frame Set of macroblocks of the previous frame used to predict the selected macroblock of the current frame Motion compensation Previous frame Current frame Macroblocks of previous frame used to predict current frame Motion compensation Each 16x16 pixel macroblock in the current frame is compared with a set of macroblocks in the previous frame to determine the one that best predicts the current macroblock. -
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 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.