Analysis of Future Emerging Video Codec's AV1, NETVC, VP10, HEVC
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Network Working Group A
Network Working Group A. Filippov Internet Draft Huawei Technologies Intended status: Informational A. Norkin Netflix J.R. Alvarez Huawei Technologies Expires: May 17, 2017 November 17, 2016 <Video Codec Requirements and Evaluation Methodology> draft-ietf-netvc-requirements-04.txt Status of this Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet- Drafts. The list of current Internet-Drafts can be accessed at http://datatracker.ietf.org/drafts/current/ Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." The list of current Internet-Drafts can be accessed at http://www.ietf.org/1id-abstracts.html The list of Internet-Draft Shadow Directories can be accessed at http://www.ietf.org/shadow.html This Internet-Draft will expire on May 17, 2017. Copyright Notice Copyright (c) 2016 IETF Trust and the persons identified as the document authors. All rights reserved. This document is subject to BCP 78 and the IETF Trust’s Legal Provisions Relating to IETF Documents (http://trustee.ietf.org/license-info) in effect on the date of publication of this document. Please review these documents carefully, as they describe your rights and restrictions with <Filippov> Expires May 17, 2017 [Page 1] Internet-Draft Video Codec Requirements and Evaluation November 2016 respect to this document. -
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 -
The Discrete Cosine Transform (DCT): Theory and Application
The Discrete Cosine Transform (DCT): 1 Theory and Application Syed Ali Khayam Department of Electrical & Computer Engineering Michigan State University March 10th 2003 1 This document is intended to be tutorial in nature. No prior knowledge of image processing concepts is assumed. Interested readers should follow the references for advanced material on DCT. ECE 802 – 602: Information Theory and Coding Seminar 1 – The Discrete Cosine Transform: Theory and Application 1. Introduction Transform coding constitutes an integral component of contemporary image/video processing applications. Transform coding relies on the premise that pixels in an image exhibit a certain level of correlation with their neighboring pixels. Similarly in a video transmission system, adjacent pixels in consecutive frames2 show very high correlation. Consequently, these correlations can be exploited to predict the value of a pixel from its respective neighbors. A transformation is, therefore, defined to map this spatial (correlated) data into transformed (uncorrelated) coefficients. Clearly, the transformation should utilize the fact that the information content of an individual pixel is relatively small i.e., to a large extent visual contribution of a pixel can be predicted using its neighbors. A typical image/video transmission system is outlined in Figure 1. The objective of the source encoder is to exploit the redundancies in image data to provide compression. In other words, the source encoder reduces the entropy, which in our case means decrease in the average number of bits required to represent the image. On the contrary, the channel encoder adds redundancy to the output of the source encoder in order to enhance the reliability of the transmission. -
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 -
DVP Tutorial
IP Video Conferencing: A Tutorial Roman Sorokin, Jean-Louis Rougier Abstract Video conferencing is a well-established area of communications, which have been studied for decades. Recently this area has received a new impulse due to significantly increased bandwidth of Local and Wide area networks, appearance of low-priced video equipment and development of web based media technologies. This paper presents the main techniques behind the modern IP-based videoconferencing services, with a particular focus on codecs, network protocols, architectures and standardization efforts. Questions of security and topologies are also tackled. A description of a typical video conference scenario is provided, demonstrating how the technologies, responsible for different conference aspects, are working together. Traditional industrial disposition as well as modern innovative approaches are both addressed. Current industry trends are highlighted in respect to the topics, described in the tutorial. Legacy analog/digital technologies, together with the gateways between the traditional and the IP videoconferencing systems, are not considered. Keywords Video Conferencing, codec, SVC, MCU, SIP, RTP Roman Sorokin ALE International, Colombes, France e-mail: [email protected] Jean-Louis Rougier Télécom ParisTech, Paris, France e-mail: [email protected] 1 1 Introduction Video conferencing is a two-way interactive communication, delivered over networks of different nature, which allows people from several locations to participate in a meeting. Conference participants use video conferencing endpoints of different types. Generally a video conference endpoint has a camera and a microphone. The video stream, generated by the camera, and the audio stream, coming from the microphone, are both compressed and sent to the network interface. -
Opus, a Free, High-Quality Speech and Audio Codec
Opus, a free, high-quality speech and audio codec Jean-Marc Valin, Koen Vos, Timothy B. Terriberry, Gregory Maxwell 29 January 2014 Xiph.Org & Mozilla What is Opus? ● New highly-flexible speech and audio codec – Works for most audio applications ● Completely free – Royalty-free licensing – Open-source implementation ● IETF RFC 6716 (Sep. 2012) Xiph.Org & Mozilla Why a New Audio Codec? http://xkcd.com/927/ http://imgs.xkcd.com/comics/standards.png Xiph.Org & Mozilla Why Should You Care? ● Best-in-class performance within a wide range of bitrates and applications ● Adaptability to varying network conditions ● Will be deployed as part of WebRTC ● No licensing costs ● No incompatible flavours Xiph.Org & Mozilla History ● Jan. 2007: SILK project started at Skype ● Nov. 2007: CELT project started ● Mar. 2009: Skype asks IETF to create a WG ● Feb. 2010: WG created ● Jul. 2010: First prototype of SILK+CELT codec ● Dec 2011: Opus surpasses Vorbis and AAC ● Sep. 2012: Opus becomes RFC 6716 ● Dec. 2013: Version 1.1 of libopus released Xiph.Org & Mozilla Applications and Standards (2010) Application Codec VoIP with PSTN AMR-NB Wideband VoIP/videoconference AMR-WB High-quality videoconference G.719 Low-bitrate music streaming HE-AAC High-quality music streaming AAC-LC Low-delay broadcast AAC-ELD Network music performance Xiph.Org & Mozilla Applications and Standards (2013) Application Codec VoIP with PSTN Opus Wideband VoIP/videoconference Opus High-quality videoconference Opus Low-bitrate music streaming Opus High-quality music streaming Opus Low-delay -
High Efficiency, Moderate Complexity Video Codec Using Only RF IPR
Thor High Efficiency, Moderate Complexity Video Codec using only RF IPR draft-fuldseth-netvc-thor-00 Arild Fuldseth, Gisle Bjontegaard (Cisco) IETF 93 – Prague, CZ – July 2015 1 Design principles • Moderate complexity to allow real-time implementation in SW on common HW, as well as new HW designs • Basic building blocks from well-known hybrid approach (motion compensated prediction and transform coding) • Common design elements in modern codecs – Larger block sizes and transforms, up to 64x64 – Quarter pixel interpolation, motion vector prediction, etc. • Cisco RF IPR (note well: declaration filed on draft) – Deblocking, transforms, etc. (some also essential in H.265/4) • Avoid non-RF IPR – If/when others offer RF IPR, design/performance will improve 2 Encoder Architecture Input Transform Quantizer Entropy Output video Coding bitstream - Inverse Transform Intra Frame Prediction Loop filters Inter Frame Prediction Reconstructed Motion Frame Estimation Memory 3 Decoder Architecture Input Entropy Inverse Bitstream Decoding Transform Intra Frame Prediction Loop filters Inter Frame Prediction Output video Reconstructed Frame Memory 4 Block Structure • Super block (SB) 64x64 • Quad-tree split into coding blocks (CB) >= 8x8 • Multiple prediction blocks (PB) per CB • Intra: 1 PB per CB • Inter: 1, 2 (rectangular) or 4 (square) PBs per CB • 1 or 4 transform blocks (TB) per CB 5 Coding-block modes • Intra • Inter0 MV index, no residual information • Inter1 MV index, residual information • Inter2 Explicit motion vector information, residual information -
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+) -
Hardware for Speech and Audio Coding
Linköping Studies in Science and Technology Thesis No. 1093 Hardware for Speech and Audio Coding Mikael Olausson LiU-TEK-LIC-2004:22 Department of Electrical Engineering Linköpings universitet, SE-581 83 Linköping, Sweden Linköping 2004 Linköping Studies in Science and Technology Thesis No. 1093 Hardware for Speech and Audio Coding Mikael Olausson LiU-TEK-LIC-2004:22 Department of Electrical Engineering Linköpings universitet, SE-581 83 Linköping, Sweden Linköping 2004 ISBN 91-7373-953-7 ISSN 0280-7971 ii Abstract While the Micro Processors (MPUs) as a general purpose CPU are converging (into Intel Pentium), the DSP processors are diverging. In 1995, approximately 50% of the DSP processors on the market were general purpose processors, but last year only 15% were general purpose DSP processors on the market. The reason general purpose DSP processors fall short to the application specific DSP processors is that most users want to achieve highest performance under mini- mized power consumption and minimized silicon costs. Therefore, a DSP proces- sor must be an Application Specific Instruction set Processor (ASIP) for a group of domain specific applications. An essential feature of the ASIP is its functional acceleration on instruction level, which gives the specific instruction set architecture for a group of appli- cations. Hardware acceleration for digital signal processing in DSP processors is essential to enhance the performance while keeping enough flexibility. In the last 20 years, researchers and DSP semiconductor companies have been working on different kinds of accelerations for digital signal processing. The trade-off be- tween the performance and the flexibility is always an interesting question because all DSP algorithms are "application specific"; the acceleration for audio may not be suitable for the acceleration of baseband signal processing. -
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. -
The Daala Video Codec Project Next-Next Generation Video
The Daala Video Codec Project Next-next Generation Video Timothy B. Terriberry Mozilla & The Xiph.Org Foundation ● Patents are no longer a problem for free software – We can all go home 2 Mozilla & The Xiph.Org Foundation ● Except... not quite 3 Mozilla & The Xiph.Org Foundation Carving out Exceptions in OIN (Table 0 contains one Xiph codec: FLAC) 4 Mozilla & The Xiph.Org Foundation Why This Matters ● Encumbered codecs are a billion dollar toll-tax on communications – Every cost from codecs is repeated a million fold in all multimedia software ● Codec licensing is anti-competitive – Licensing regimes are universally discriminatory – An excuse for proprietary software (Flash) ● Ignoring licensing creates risks that can show up at any time – A tax on success 5 Mozilla & The Xiph.Org Foundation The Royalty-Free Video Challenge ● Creating good codecs is hard – But we don’t need many – The best implementations of patented codecs are already free software ● Network effects decide – Where RF is established, non-free codecs see no adoption (JPEG, PNG, FLAC, …) ● RF is not enough – People care about different things – Must be better on all fronts 6 Mozilla & The Xiph.Org Foundation We Did This for Audio 7 Mozilla & The Xiph.Org Foundation The Daala Project ● Goal: Better than HEVC without infringing IPR ● Need a better strategy than “read a lot of patents” – People don’t believe you – Analysis is error-prone ● Try to stay far away from the line, but... ● One mistake can ruin years of development effort ● See: H.264 Baseline 8 Mozilla & The Xiph.Org