Introduction to H.264 Advanced Video Coding
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Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1 -
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. -
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 -
Annual Report 2016
ANNUAL REPORT 2016 PUNJABI UNIVERSITY, PATIALA © Punjabi University, Patiala (Established under Punjab Act No. 35 of 1961) Editor Dr. Shivani Thakar Asst. Professor (English) Department of Distance Education, Punjabi University, Patiala Laser Type Setting : Kakkar Computer, N.K. Road, Patiala Published by Dr. Manjit Singh Nijjar, Registrar, Punjabi University, Patiala and Printed at Kakkar Computer, Patiala :{Bhtof;Nh X[Bh nk;k wjbk ñ Ò uT[gd/ Ò ftfdnk thukoh sK goT[gekoh Ò iK gzu ok;h sK shoE tk;h Ò ñ Ò x[zxo{ tki? i/ wB[ bkr? Ò sT[ iw[ ejk eo/ w' f;T[ nkr? Ò ñ Ò ojkT[.. nk; fBok;h sT[ ;zfBnk;h Ò iK is[ i'rh sK ekfJnk G'rh Ò ò Ò dfJnk fdrzpo[ d/j phukoh Ò nkfg wo? ntok Bj wkoh Ò ó Ò J/e[ s{ j'fo t/; pj[s/o/.. BkBe[ ikD? u'i B s/o/ Ò ô Ò òõ Ò (;qh r[o{ rqzE ;kfjp, gzBk óôù) English Translation of University Dhuni True learning induces in the mind service of mankind. One subduing the five passions has truly taken abode at holy bathing-spots (1) The mind attuned to the infinite is the true singing of ankle-bells in ritual dances. With this how dare Yama intimidate me in the hereafter ? (Pause 1) One renouncing desire is the true Sanayasi. From continence comes true joy of living in the body (2) One contemplating to subdue the flesh is the truly Compassionate Jain ascetic. Such a one subduing the self, forbears harming others. (3) Thou Lord, art one and Sole. -
05-08-321Video.Pdf
Topics Covered Overview – What is Videography? Videography Roles Writing and Speaking Skills for Video Storyboarding Scripting Camera Basics Filming & Filming Techniques 3,2,1..Action! Video Video Roles Producer Keeps the video production on schedule. Director Directs ‘on-air’ talent, works with Videographer. Screenwriter Writes the script, copy, (scriptwriter) narration or voice-over for film. Videographer (camera Photographs the person) scenes and works with the Director. Video Roles Graphic Artist Designs and creates visuals to enhance and support the message. Wardrobe (and Helps dress actors in Makeup) Director approved costumes and applies makeup. Puts scenes together to Editor most effectively convey the video’s message. Works with director, writers Reporter/Interviewer and interviewers to produce – Narrator/Presenter the best possible responses to the Director. Writing Skills for Video Write clearly and simply Use Conversational tone Keep sentences short. First and second person (“I/You can make a difference”.) Use action verbs & present tense (“join”, “come”, “write”, “buy”) Use comparisons & metaphors (indirect comparison) Narrated copy; 60-sec spot = 75-100 words Speaking Skills for Video Use a strong, confident, voice. Enunciate, speak clearly. Use appropriate facial expressions. Eye contact – extremely important and powerful. Use of hand gestures – ‘only if appropriate’. Microphone placement Watch network newscaster- note pitch, speed, facial expressions, gestures. Writing & Speaking Activity Write out the following words: You should do for your country as you would want your country to do for you. To the person on your left – speak these words (in this order) in your best Narrator or Reporter voice. Rewrite the above words into a more emphatic, emotional, and powerful message. -
Arithmetic Coding
Arithmetic Coding Arithmetic coding is the most efficient method to code symbols according to the probability of their occurrence. The average code length corresponds exactly to the possible minimum given by information theory. Deviations which are caused by the bit-resolution of binary code trees do not exist. In contrast to a binary Huffman code tree the arithmetic coding offers a clearly better compression rate. Its implementation is more complex on the other hand. In arithmetic coding, a message is encoded as a real number in an interval from one to zero. Arithmetic coding typically has a better compression ratio than Huffman coding, as it produces a single symbol rather than several separate codewords. Arithmetic coding differs from other forms of entropy encoding such as Huffman coding in that rather than separating the input into component symbols and replacing each with a code, arithmetic coding encodes the entire message into a single number, a fraction n where (0.0 ≤ n < 1.0) Arithmetic coding is a lossless coding technique. There are a few disadvantages of arithmetic coding. One is that the whole codeword must be received to start decoding the symbols, and if there is a corrupt bit in the codeword, the entire message could become corrupt. Another is that there is a limit to the precision of the number which can be encoded, thus limiting the number of symbols to encode within a codeword. There also exist many patents upon arithmetic coding, so the use of some of the algorithms also call upon royalty fees. Arithmetic coding is part of the JPEG data 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. -
Shiffman 120, 122, 123, 125, 201, 202, 217 Classroom Instructions
Shiffman 120, 122, 123, 125, 201, 202, 217 (TEC classrooms – level B) No Access Codes Needed. These rooms utilize ceiling-mounted projectors, focused on wall-mounted pull down screens. Displaying Video or Computer Sources 1. At the Instructor Station. Push DOWN on the black system controller to access controls. Touch the screen to begin. 2. Press the PC (or any source) button to turn on the display system. The projector will take over a minute to warm up. No controls will function during warm up. 3. After the warm up, you can select another source using the appropriate buttons. (Buttons illuminate when active.) It’s recommended to wait a few seconds between input selections. • PC will display the installed Instructor PC. There are courtesy USB ports on the front of the PC. • LAPTOP will open another page, where you can select the VGA cable w/audio or the Digital HDMI cable as your laptop source. These cables are provided at the instructor station. Beyond the buttons, the system will attempt to auto-detect, and switch to a laptop when properly connected. Users must provide their MAC & DisplayPort adapters. • DVD selects the DVD player, stored at the Instructor Station. A disc control page will appear on the touch panel allowing you to control the player. • If a VHS player exists in your room, a VCR button will appear as a source on the touch panel. Press VCR to select. If there is no VHS player, contact Media Technology Services (MTS: 781- 736-4632) to request a temporary unit. Legacy VHS stock is declining, and this is based on inventory availability. -
(12) United States Patent (10) Patent No.: US 7,590,772 B2 Marriott Et Al
US00759.0772B2 (12) United States Patent (10) Patent No.: US 7,590,772 B2 Marriott et al. (45) Date of Patent: Sep. 15, 2009 (54) AUDIO STATUS INFORMATION FOR A 5,596.260 A 1/1997 Moravec et al. PORTABLE ELECTRONIC DEVICE 5,608,698 A 3, 1997 Yamanoi et al. 5,616,876 A 4, 1997 ClutS (75) Inventors: Greg Marriott, Palo Alto, CA (US); 3.65 A 2. ls. Sr. etal Andrew Bert Hodge, Menlo Park, CA 5,684.513.w W A 1 1/1997 Deckerappels, Sr. et al. 5,710,922 A 1/1998 Alley et al. 5,712,949 A 1/1998 Kato et al. (73) Assignee: Apple Inc., Cupertino, CA (US) 5,721,949 A 2f1998 Smith et al. c 5,726,672 A 3, 1998 Hernandez et al. (*) Notice: Subject to any disclaimer, the term of this 5,739,451 A 4/1998 Winksy et al. patent is extended or adjusted under 35 5,740,143 A 4/1998 Suetomi U.S.C. 154(b) by 344 days. 5,815,225. A 9/1998 Nelson (21) Appl. No.: 11/209,367 (Continued) (22) Filed: Aug.e 22,a? a 9 2005 FOREIGN PATENT DOCUMENTS DE 43 34 773 A1 4f1994 (65) Prior Publication Data US 2007/0079027 A1 Apr. 5, 2007 (Continued) (51) Int. Cl OTHER PUBLICATIONS nt. C. G06F 3/00 (2006.01) U.S. Appl. No. 1 1/144,541, filed Jun. 3, 2005 and titled “Techniques G06F I/00 (2006.01) for Presenting Sound Effects on a Portable Media Player.” (52) U.S. -
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 -
Information Theory Revision (Source)
ELEC3203 Digital Coding and Transmission – Overview & Information Theory S Chen Information Theory Revision (Source) {S(k)} {b i } • Digital source is defined by digital source source coding 1. Symbol set: S = {mi, 1 ≤ i ≤ q} symbols/s bits/s 2. Probability of occurring of mi: pi, 1 ≤ i ≤ q 3. Symbol rate: Rs [symbols/s] 4. Interdependency of {S(k)} • Information content of alphabet mi: I(mi) = − log2(pi) [bits] • Entropy: quantifies average information conveyed per symbol q – Memoryless sources: H = − pi · log2(pi) [bits/symbol] i=1 – 1st-order memory (1st-order Markov)P sources with transition probabilities pij q q q H = piHi = − pi pij · log2(pij) [bits/symbol] Xi=1 Xi=1 Xj=1 • Information rate: tells you how many bits/s information the source really needs to send out – Information rate R = Rs · H [bits/s] • Efficient source coding: get rate Rb as close as possible to information rate R – Memoryless source: apply entropy coding, such as Shannon-Fano and Huffman, and RLC if source is binary with most zeros – Generic sources with memory: remove redundancy first, then apply entropy coding to “residauls” 86 ELEC3203 Digital Coding and Transmission – Overview & Information Theory S Chen Practical Source Coding • Practical source coding is guided by information theory, with practical constraints, such as performance and processing complexity/delay trade off • When you come to practical source coding part, you can smile – as you should know everything • As we will learn, data rate is directly linked to required bandwidth, source coding is to encode source with a data rate as small as possible, i.e. -
Multiple Reference Motion Compensation: a Tutorial Introduction and Survey Contents
Foundations and TrendsR in Signal Processing Vol. 2, No. 4 (2008) 247–364 c 2009 A. Leontaris, P. C. Cosman and A. M. Tourapis DOI: 10.1561/2000000019 Multiple Reference Motion Compensation: A Tutorial Introduction and Survey By Athanasios Leontaris, Pamela C. Cosman and Alexis M. Tourapis Contents 1 Introduction 248 1.1 Motion-Compensated Prediction 249 1.2 Outline 254 2 Background, Mosaic, and Library Coding 256 2.1 Background Updating and Replenishment 257 2.2 Mosaics Generated Through Global Motion Models 261 2.3 Composite Memories 264 3 Multiple Reference Frame Motion Compensation 268 3.1 A Brief Historical Perspective 268 3.2 Advantages of Multiple Reference Frames 270 3.3 Multiple Reference Frame Prediction 271 3.4 Multiple Reference Frames in Standards 277 3.5 Interpolation for Motion Compensated Prediction 281 3.6 Weighted Prediction and Multiple References 284 3.7 Scalable and Multiple-View Coding 286 4 Multihypothesis Motion-Compensated Prediction 290 4.1 Bi-Directional Prediction and Generalized Bi-Prediction 291 4.2 Overlapped Block Motion Compensation 294 4.3 Hypothesis Selection Optimization 296 4.4 Multihypothesis Prediction in the Frequency Domain 298 4.5 Theoretical Insight 298 5 Fast Multiple-Frame Motion Estimation Algorithms 301 5.1 Multiresolution and Hierarchical Search 302 5.2 Fast Search using Mathematical Inequalities 303 5.3 Motion Information Re-Use and Motion Composition 304 5.4 Simplex and Constrained Minimization 306 5.5 Zonal and Center-biased Algorithms 307 5.6 Fractional-pixel Texture Shifts or Aliasing