Versatile Video Coding Standard
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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 -
Overview of Technologies for Immersive Visual Experiences: Capture, Processing, Compression, Standardization and Display
Overview of technologies for immersive visual experiences: capture, processing, compression, standardization and display Marek Domański Poznań University of Technology Chair of Multimedia Telecommunications and Microelectronics Poznań, Poland 1 Immersive visual experiences • Arbitrary direction of viewing System : 3DoF • Arbitrary location of viewer - virtual navigation - free-viewpoint television • Both System : 6DoF Virtual viewer 2 Immersive video content Computer-generated Natural content Multiple camera around a scene also depth cameras, light-field cameras Camera(s) located in a center of a scene 360-degree cameras Mixed e.g.: wearable cameras 3 Video capture Common technical problems • Synchronization of cameras Camera hardware needs to enable synchronization Shutter release error < Exposition interval • Frame rate High frame rate needed – Head Mounted Devices 4 Common task for immersive video capture: Calibration Camera parameter estimation: • Intrinsic – the parameters of individual cameras – remain unchanged by camera motion • Extrinsic – related to camera locations in real word – do change by camera motion (even slight !) Out of scope of standardization • Improved methods developed 5 Depth estimation • By video analysis – from at least 2 video sequences – Computationally heavy – Huge progress recently • By depth cameras – diverse products – Infrared illuminate of the scene – Limited resolution mostly Out of scope of standardization 6 MPEG Test video sequences • Large collection of video sequences with depth information -
Versatile Video Coding (VVC)
Versatile Video Coding (VVC) on the final stretch Benjamin Bross Fraunhofer Heinrich Hertz Institute, Berlin ITU Workshop on “The future of media” © Geneva, Switzerland, 8 October 2019 Versatile Video Coding (VVC) Joint ITU-T (VCEG) and ISO/IEC (MPEG) project Coding Efficiency Versatility 50% over H.265/HEVC Screen content HD / UHD / 8K resolutions Adaptive resolution change 10bit / HDR Independent sub-pictures 2 VVC – Coding Efficiency History of Video Coding Standards H.261 JPEG H.265 / H.264 / H.262 / (1991) (1990) PSNR MPEG-HEVC MPEG-4 AVC MPEG-2 (dB) (2013) (2003) (1995) 40 38 Bit-rate Reduction: 50% 35 36 34 32 30 28 0 100 200 300 bit rate (kbit/s) 3 VVC – Coding Efficiency History of Video Coding Standards H.261 JPEG H.265 / H.264 / H.262 / (1991) (1990) PSNR MPEG-HEVC MPEG-4 AVC MPEG-2 (dB) (2013) (2003) (1995) 40 38 36 Do we need more efficient video coding? 34 32 30 28 0 100 200 300 bit rate (kbit/s) 4 VVC – Coding Efficiency Jevons Paradox "The efficiency with which a resource is used tends to increase (rather than decrease) the rate of consumption of that resource." 5 VVC – Coding Efficiency Target for the final VVC standard H.262 / H.261 JPEG H.??? / H.265 / H.264 / MPEG-2 (1991) (1990) PSNR MPEG-VVC MPEG-HEVC MPEG-4 AVC (dB) (2013) (2003) (1995) 40 38 36 35 Bit-rate Reduction Target: 50% 34 32 30 28 0 100 200 300 bit rate (kbit/s) 6 VVC – Timeline 2015 Oct. – Exploration Phase • Joint Video Exploration Team (JVET) of ITU-T VCEG and ISO/IEC MPEG established October ‘15 in Geneva • Joint Video Exploration Model (JEM) as software playground to explore new coding tools • 34% bitrate savings for JEM relative to HEVC provided evidence to start a new joint standardization activity with a… 2017 Oct. -
On Audio-Visual File Formats
On Audio-Visual File Formats Summary • digital audio and digital video • container, codec, raw data • different formats for different purposes Reto Kromer • AV Preservation by reto.ch • audio-visual data transformations Film Preservation and Restoration Hyderabad, India 8–15 December 2019 1 2 Digital Audio • sampling Digital Audio • quantisation 3 4 Sampling • 44.1 kHz • 48 kHz • 96 kHz • 192 kHz digitisation = sampling + quantisation 5 6 Quantisation • 16 bit (216 = 65 536) • 24 bit (224 = 16 777 216) • 32 bit (232 = 4 294 967 296) Digital Video 7 8 Digital Video Resolution • resolution • SD 480i / SD 576i • bit depth • HD 720p / HD 1080i • linear, power, logarithmic • 2K / HD 1080p • colour model • 4K / UHD-1 • chroma subsampling • 8K / UHD-2 • illuminant 9 10 Bit Depth Linear, Power, Logarithmic • 8 bit (28 = 256) «medium grey» • 10 bit (210 = 1 024) • linear: 18% • 12 bit (212 = 4 096) • power: 50% • 16 bit (216 = 65 536) • logarithmic: 50% • 24 bit (224 = 16 777 216) 11 12 Colour Model • XYZ, L*a*b* • RGB / R′G′B′ / CMY / C′M′Y′ • Y′IQ / Y′UV / Y′DBDR • Y′CBCR / Y′COCG • Y′PBPR 13 14 15 16 17 18 RGB24 00000000 11111111 00000000 00000000 00000000 00000000 11111111 00000000 00000000 00000000 00000000 11111111 00000000 11111111 11111111 11111111 11111111 00000000 11111111 11111111 11111111 11111111 00000000 11111111 19 20 Compression Uncompressed • uncompressed + data simpler to process • lossless compression + software runs faster • lossy compression – bigger files • chroma subsampling – slower writing, transmission and reading • born -
Videocodecs in a Nutshell
Videocodecs in a Nutshell Wie funktioniert’s und was gibt’s neues? darkbit 14.08.2021 14.08.21 1 Gliederung ● Motivation ● Grundlagen moderner Videocodecs am Beispiel von HEVC ● Neue Videocodecs – AOMedia Video 1 (AV1) – Versatile Video Coding (VVC) – Essential Video Coding (EVC) – Low Complexity Enhancement Video Codec (LC EVC) ● Vergleich der Codecs ● Ausblick 14.08.21 Videocodecs in a Nutshell - darkbit 2 Motivation ● Rohvideo ist groß ● Bsp: Spielfilm auf DVD – Pixel hat je 8 Bit für die RGB-Komponenten => 24 bits pro Pixel – Übliche Auflösung 720x576 Pixel => 414‘720 Pixel => ~9,953Mbit pro Bild – 25 Bilder pro Sekunde => 248,825 Mbit/s – 90 Minuten Spielfilmlänge => 1,344 Tbit (168 Gbyte) pro Film – Wir haben aber nur max. 8,5 Gbyte pro DVD (Dual-Layer) – Wir müssen unser Video um mind. 95% komprimieren! ● Ziel eines Videocodecs: Möglichst hohe Kompression bei möglichst geringer visuellen Qualitätseinbußungen. 14.08.21 Videocodecs in a Nutshell - darkbit 3 Motivation CC-BY 3.0 - Blender Foundation CC-BY-SA 3.0 - Rdikeman Anforderungen On-Demand Video Live Video Kompression möglichst hoch auf Kanalbandbreite Encodingspeed irrelevant in Echtzeit Decodingspeed in Echtzeit in Echtzeit Bitrate adaptiv konstant 14.08.21 Videocodecs in a Nutshell - darkbit 4 State of the Art Videocodecs Veröffentlichung Codec Bitraten-reduktion Anwendungen Mai 1996 H.262, MPEG-2 Part 2 DVD, SDTV, Blu-Ray März 2003 AVC (H.264, MPEG-4 -50% gegenüber HDTV, Webvideo, Part 10) H.262 WebRTC, Blu-Ray September 2008 (seit VP8 -5% gegenüber AVC Webvideo, 2010 lizenzfrei) WebRTC Mai 2013 VP9 -20% gegenüber AVC Webvideo, WebRTC Dezember 2013 HEVC (H.265, MPEG-H -20% gegenüber AVC UHD Blu-Ray Part 2) 14.08.21 Videocodecs in a Nutshell - darkbit 5 Patentproblematiken Quelle: Jonatan Samuelsson und Per Hermansson. -
VOL. E100-C NO. 6 JUNE 2017 the Usage of This PDF File Must Comply
VOL. E100-C NO. 6 JUNE 2017 The usage of this PDF file must comply with the IEICE Provisions on Copyright. The author(s) can distribute this PDF file for research and educational (nonprofit) purposes only. Distribution by anyone other than the author(s) is prohibited. IEICE TRANS. ELECTRON., VOL.E100–C, NO.6 JUNE 2017 643 PAPER A High-Throughput and Compact Hardware Implementation for the Reconstruction Loop in HEVC Intra Encoding Yibo FAN†a), Member, Leilei HUANG†, Zheng XIE†, and Xiaoyang ZENG†, Nonmembers SUMMARY In the newly finalized video coding standard, namely high 4×4, 8×8, 16×16, 32×32 and 64×64 with 35 possible pre- efficiency video coding (HEVC), new notations like coding unit (CU), pre- diction modes in intra prediction. Although several fast diction unit (PU) and transformation unit (TU) are introduced to improve mode decision designs have been proposed, still a consid- the coding performance. As a result, the reconstruction loop in intra en- coding is heavily burdened to choose the best partitions or modes for them. erable amount of candidate PU modes, PU partitions or TU In order to solve the bottleneck problems in cycle and hardware cost, this partitions are needed to be traversed by the reconstruction paper proposed a high-throughput and compact implementation for such a loop. reconstruction loop. By “high-throughput”, it refers to that it has a fixed It can be inferred that the reconstruction loop in intra throughput of 32 pixel/cycle independent of the TU/PU size (except for 4×4 TUs). By “compact”, it refers to that it fully explores the reusability prediction has become a bottleneck in cycle and hardware between discrete cosine transform (DCT) and inverse discrete cosine trans- cost. -
Amphion Video Codecs in an AI World
Video Codecs In An AI World Dr Doug Ridge Amphion Semiconductor The Proliferance of Video in Networks • Video produces huge volumes of data • According to Cisco “By 2021 video will make up 82% of network traffic” • Equals 3.3 zetabytes of data annually • 3.3 x 1021 bytes • 3.3 billion terabytes AI Engines Overview • Example AI network types include Artificial Neural Networks, Spiking Neural Networks and Self-Organizing Feature Maps • Learning and processing are automated • Processing • AI engines designed for processing huge amounts of data quickly • High degree of parallelism • Much greater performance and significantly lower power than CPU/GPU solutions • Learning and Inference • AI ‘learns’ from masses of data presented • Data presented as Input-Desired Output or as unmarked input for self-organization • AI network can start processing once initial training takes place Typical Applications of AI • Reduce data to be sorted manually • Example application in analysis of mammograms • 99% reduction in images send for analysis by specialist • Reduction in workload resulted in huge reduction in wrong diagnoses • Aid in decision making • Example application in traffic monitoring • Identify areas of interest in imagery to focus attention • No definitive decision made by AI engine • Perform decision making independently • Example application in security video surveillance • Alerts and alarms triggered by AI analysis of behaviours in imagery • Reduction in false alarms and more attention paid to alerts by security staff Typical Video Surveillance -
Encoding H.264 Video for Streaming and Progressive Download
W4: KEY ENCODING SKILLS, TECHNOLOGIES TECHNIQUES STREAMING MEDIA EAST - 2019 Jan Ozer www.streaminglearningcenter.com [email protected]/ 276-235-8542 @janozer Agenda • Introduction • Lesson 5: How to build encoding • Lesson 1: Delivering to Computers, ladder with objective quality metrics Mobile, OTT, and Smart TVs • Lesson 6: Current status of CMAF • Lesson 2: Codec review • Lesson 7: Delivering with dynamic • Lesson 3: Delivering HEVC over and static packaging HLS • Lesson 4: Per-title encoding Lesson 1: Delivering to Computers, Mobile, OTT, and Smart TVs • Computers • Mobile • OTT • Smart TVs Choosing an ABR Format for Computers • Can be DASH or HLS • Factors • Off-the-shelf player vendor (JW Player, Bitmovin, THEOPlayer, etc.) • Encoding/transcoding vendor Choosing an ABR Format for iOS • Native support (playback in the browser) • HTTP Live Streaming • Playback via an app • Any, including DASH, Smooth, HDS or RTMP Dynamic Streaming iOS Media Support Native App Codecs H.264 (High, Level 4.2), HEVC Any (Main10, Level 5 high) ABR formats HLS Any DRM FairPlay Any Captions CEA-608/708, WebVTT, IMSC1 Any HDR HDR10, DolbyVision ? http://bit.ly/hls_spec_2017 iOS Encoding Ladders H.264 HEVC http://bit.ly/hls_spec_2017 HEVC Hardware Support - iOS 3 % bit.ly/mobile_HEVC http://bit.ly/glob_med_2019 Android: Codec and ABR Format Support Codecs ABR VP8 (2.3+) • Multiple codecs and ABR H.264 (3+) HLS (3+) technologies • Serious cautions about HLS • DASH now close to 97% • HEVC VP9 (4.4+) DASH 4.4+ Via MSE • Main Profile Level 3 – mobile HEVC (5+) -
2021 TIW DRAFT AGENDA Revi
24th ITEA Test and Training Instrumentation Workshop Innovating for Tomorrow’s Challenges 5/5/21 REV I 11-May First Day – Tutorials 8:00 a.m. – 12:00 p.m. Morning Tutorials Basics of Aircraft Instrumentation Systems Bruce Johnson, NAWCAD This course will cover a wide variety of topics related to Aircraft Instrumentation. Data, Telemetry, Instrumentation System Block Diagram, Standards, Data Requirements, Transducers / Specifications, Video, 1553 Bus, Using Requirements to Configure an Analog Data Channel, Creating a PCM Map to Obtain a Sample Rate, Telemetry Bandwidth, Record Time, GPS, Audio, Telemetry Attributes Transfer Standard (TMATS), and Measurement Uncertainty - Interpreting the Results. This is great introduction for new hires or a refresher for current employees. IRIG 106-17 Chapter 7 Packet Telemetry Downlink Basis and Implementation Fundamentals Johnny Pappas, Safran Data Systems, Inc. This course will focus on presenting information to establish a basic understanding of the 2017 release of the IRIG 106, Chapter 7, Packet Telemetry Downlink Standard. It will also focus on the implementation of airborne and ground system hardware and methods to handle IRIG 106, Chapter 7, Packet Telemetry data. The presentation will address the implementation of special features necessary to support legacy RF Transmission, data recording, RF Receiving, Ground Reproduction, and Chapter 10 data processing methods. Predictive Analytics for Performance Assessment Mark J. Kiemele, Air Academy Associates Design of Experiments (DOE) is a method that can and should be used not only in the design and development of systems, but also in the modeling and validation of system performance. Building useful prediction models and then validating them can ease the burden of making procurement decisions. -
Dynamic Resource Management of Network-On-Chip Platforms for Multi-Stream Video Processing
DYNAMIC RESOURCE MANAGEMENT OF NETWORK-ON-CHIP PLATFORMS FOR MULTI-STREAM VIDEO PROCESSING Hashan Roshantha Mendis Doctor of Engineering University of York Computer Science March 2017 2 Abstract This thesis considers resource management in the context of parallel multiple video stream de- coding, on multicore/many-core platforms. Such platforms have tens or hundreds of on-chip processing elements which are connected via a Network-on-Chip (NoC). Inefficient task allo- cation configurations can negatively affect the communication cost and resource contention in the platform, leading to predictability and performance issues. Efficient resource management for large-scale complex workloads is considered a challenging research problem; especially when applications such as video streaming and decoding have dynamic and unpredictable workload characteristics. For these type of applications, runtime heuristic-based task mapping techniques are required. As the application and platform size increase, decentralised resource management techniques are more desirable to overcome the reliability and performance bot- tlenecks in centralised management. In this work, several heuristic-based runtime resource management techniques, targeting real-time video decoding workloads are proposed. Firstly, two admission control approaches are proposed; one fully deterministic and highly predictable; the other is heuristic-based, which balances predictability and performance. Secondly, a pair of runtime task mapping schemes are presented, which make use of limited known application properties, communication cost and blocking-aware heuristics. Combined with the proposed deterministic admission con- troller, these techniques can provide strict timing guarantees for hard real-time streams whilst improving resource usage. The third contribution in this thesis is a distributed, bio-inspired, low-overhead, task re-allocation technique, which is used to further improve the timeliness and workload distribution of admitted soft real-time streams. -
Parameter Optimization in H.265 Rate-Distortion by Single Frame
https://doi.org/10.2352/ISSN.2470-1173.2019.11.IPAS-262 © 2019, Society for Imaging Science and Technology Parameter optimization in H.265 Rate-Distortion by single- frame semantic scene analysis Ahmed M. Hamza; University of Portsmouth Abdelrahman Abdelazim; Blackpool and the Fylde College Djamel Ait-Boudaoud; University of Portsmouth Abstract with Q being the quantization value for the source. The relation is The H.265/HEVC (High Efficiency Video Coding) codec and derived based on several assumptions about the source probability its 3D extensions have crucial rate-distortion mechanisms that distribution within the quantization intervals, and the nature of help determine coding efficiency. We have introduced in this the rate-distortion relations themselves (constantly differentiable work a new system of Lagrangian parameterization in RDO cost throughout, etc.). The value initially used for c in the literature functions, based on semantic cues in an image, starting with the was 0.85. This was modified and made adaptive in subsequent current HEVC formulation of the Lagrangian hyper-parameter standards including HEVC/H.265. heuristics. Two semantic scenery flag algorithms are presented Our work here investigates further adaptations to the rate- and tested within the Lagrangian formulation as weighted factors. distortion Lagrangian by semantic algorithms, made possible by The investigation of whether the semantic gap between the coder recent frameworks in computer vision. and the image content is holding back the block-coding mecha- nisms as a whole from achieving greater efficiency has yielded a Adaptive Lambda Hyper-parameters in HEVC positive answer. Early versions of the rate-distortion parameters in H.263 were replaced with more sophisticated models in subsequent stan- Introduction dards. -
IP-Soc Shanghai 2017 ALLEGRO Presentation FINAL
Building an Area-optimized Multi-format Video Encoder IP Tomi Jalonen VP Sales www.allegrodvt.com Allegro DVT Founded in 2003 Privately owned, based in Grenoble (France) Two product lines: 1) Industry de-facto standard video compliance streams Decoder syntax, performance and error resilience streams for H.264|MVC, H.265/SHVC, VP9, AVS2 and AV1 System compliance streams 2) Leading semiconductor video IP Multi-format encoder IP for H.264, H.265, VP9, JPEG Multi-format decoder IP for H.264, H.265, VP9, JPEG WiGig IEEE 802.11ad WDE CODEC IP 2 Evolution of Video Coding Standards International standards defined by standardization bodies such as ITU-T and ISO/IEC H.261 (1990) MPEG-1 (1993) H.262 / MPEG-2 (1995) H.263 (1996) MPEG-4 Part 2 (1999) H.264 / AVC / MPEG-4 Part 10 (2003) H.265 / HEVC (2013) Future Video Coding (“FVC”) MPEG and ISO "Preliminary Joint Call for Evidence on Video Compression with Capability beyond HEVC.” (202?) Incremental improvements of transform-based & motion- compensated hybrid video coding schemes to meet the ever increasing resolution and frame rate requirements 3 Regional Video Standards SMPTE standards in the US VC-1 (2006) VC-2 (2008) China Information Industry Department standards AVS (2005) AVS+ (2012) AVS2.0 (2016) 4 Proprietary Video Formats Sorenson Spark On2 VP6, VP7 RealVideo DivX Popular in the past partly due to technical merits but mainly due to more suitable licensing schemes to a given application than standard video video formats with their patent royalties. 5 Royalty-free Video Formats Xiph.org Foundation