AV1: Implementation, Performance and Applications Jerome Vieron and Ian Trow
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Thumbor-Video-Engine Release 1.2.2
thumbor-video-engine Release 1.2.2 Aug 14, 2021 Contents 1 Installation 3 2 Setup 5 2.1 Contents.................................................6 2.2 License.................................................. 13 2.3 Indices and tables............................................ 13 i ii thumbor-video-engine, Release 1.2.2 thumbor-video-engine provides a thumbor engine that can read, crop, and transcode audio-less video files. It supports input and output of animated GIF, animated WebP, WebM (VP9) video, and MP4 (default H.264, but HEVC is also supported). Contents 1 thumbor-video-engine, Release 1.2.2 2 Contents CHAPTER 1 Installation pip install thumbor-video-engine Go to GitHub if you need to download or install from source, or to report any issues. 3 thumbor-video-engine, Release 1.2.2 4 Chapter 1. Installation CHAPTER 2 Setup In your thumbor configuration file, change the ENGINE setting to 'thumbor_video_engine.engines. video' to enable video support. This will allow thumbor to support video files in addition to whatever image formats it already supports. If the file passed to thumbor is an image, it will use the Engine specified by the configuration setting IMAGING_ENGINE (which defaults to 'thumbor.engines.pil'). To enable transcoding between formats, add 'thumbor_video_engine.filters.format' to your FILTERS setting. If 'thumbor.filters.format' is already present, replace it with the filter from this pack- age. ENGINE = 'thumbor_video_engine.engines.video' FILTERS = [ 'thumbor_video_engine.filters.format', 'thumbor_video_engine.filters.still', ] To enable automatic transcoding to animated gifs to webp, you can set FFMPEG_GIF_AUTO_WEBP to True. To use this feature you cannot set USE_GIFSICLE_ENGINE to True; this causes thumbor to bypass the custom ENGINE altogether. -
Digital Video Quality Handbook (May 2013
Digital Video Quality Handbook May 2013 This page intentionally left blank. Executive Summary Under the direction of the Department of Homeland Security (DHS) Science and Technology Directorate (S&T), First Responders Group (FRG), Office for Interoperability and Compatibility (OIC), the Johns Hopkins University Applied Physics Laboratory (JHU/APL), worked with the Security Industry Association (including Steve Surfaro) and members of the Video Quality in Public Safety (VQiPS) Working Group to develop the May 2013 Video Quality Handbook. This document provides voluntary guidance for providing levels of video quality in public safety applications for network video surveillance. Several video surveillance use cases are presented to help illustrate how to relate video component and system performance to the intended application of video surveillance, while meeting the basic requirements of federal, state, tribal and local government authorities. Characteristics of video surveillance equipment are described in terms of how they may influence the design of video surveillance systems. In order for the video surveillance system to meet the needs of the user, the technology provider must consider the following factors that impact video quality: 1) Device categories; 2) Component and system performance level; 3) Verification of intended use; 4) Component and system performance specification; and 5) Best fit and link to use case(s). An appendix is also provided that presents content related to topics not covered in the original document (especially information related to video standards) and to update the material as needed to reflect innovation and changes in the video environment. The emphasis is on the implications of digital video data being exchanged across networks with large numbers of components or participants. -
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 -
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 -
(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. -
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 -
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+) -
On the Accuracy of Video Quality Measurement Techniques
On the accuracy of video quality measurement techniques Deepthi Nandakumar Yongjun Wu Hai Wei Avisar Ten-Ami Amazon Video, Bangalore, India Amazon Video, Seattle, USA Amazon Video, Seattle, USA Amazon Video, Seattle, USA, [email protected] [email protected] [email protected] [email protected] Abstract —With the massive growth of Internet video and therefore measuring and optimizing for video quality in streaming, it is critical to accurately measure video quality these high quality ranges is an imperative for streaming service subjectively and objectively, especially HD and UHD video which providers. In this study, we lay specific emphasis on the is bandwidth intensive. We summarize the creation of a database measurement accuracy of subjective and objective video quality of 200 clips, with 20 unique sources tested across a variety of scores in this high quality range. devices. By classifying the test videos into 2 distinct quality regions SD and HD, we show that the high correlation claimed by objective Globally, a healthy mix of devices with different screen sizes video quality metrics is led mostly by videos in the SD quality and form factors is projected to contribute to IP traffic in 2021 region. We perform detailed correlation analysis and statistical [1], ranging from smartphones (44%), to tablets (6%), PCs hypothesis testing of the HD subjective quality scores, and (19%) and TVs (24%). It is therefore necessary for streaming establish that the commonly used ACR methodology of subjective service providers to quantify the viewing experience based on testing is unable to capture significant quality differences, leading the device, and possibly optimize the encoding and delivery to poor measurement accuracy for both subjective and objective process accordingly. -
The H.264 Advanced Video Coding (AVC) Standard
Whitepaper: The H.264 Advanced Video Coding (AVC) Standard What It Means to Web Camera Performance Introduction A new generation of webcams is hitting the market that makes video conferencing a more lifelike experience for users, thanks to adoption of the breakthrough H.264 standard. This white paper explains some of the key benefits of H.264 encoding and why cameras with this technology should be on the shopping list of every business. The Need for Compression Today, Internet connection rates average in the range of a few megabits per second. While VGA video requires 147 megabits per second (Mbps) of data, full high definition (HD) 1080p video requires almost one gigabit per second of data, as illustrated in Table 1. Table 1. Display Resolution Format Comparison Format Horizontal Pixels Vertical Lines Pixels Megabits per second (Mbps) QVGA 320 240 76,800 37 VGA 640 480 307,200 147 720p 1280 720 921,600 442 1080p 1920 1080 2,073,600 995 Video Compression Techniques Digital video streams, especially at high definition (HD) resolution, represent huge amounts of data. In order to achieve real-time HD resolution over typical Internet connection bandwidths, video compression is required. The amount of compression required to transmit 1080p video over a three megabits per second link is 332:1! Video compression techniques use mathematical algorithms to reduce the amount of data needed to transmit or store video. Lossless Compression Lossless compression changes how data is stored without resulting in any loss of information. Zip files are losslessly compressed so that when they are unzipped, the original files are recovered. -
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 -
PVQ Applied Outside of Daala (IETF 97 Draft)
PVQ Applied outside of Daala (IETF 97 Draft) Yushin Cho Mozilla Corporation November, 2016 Introduction ● Perceptual Vector Quantization (PVQ) – Proposed as a quantization and coefficient coding tool for NETVC – Originally developed for the Daala video codec – Does a gain-shape coding of transform coefficients ● The most distinguishing idea of PVQ is the way it references a predictor. – PVQ does not subtract the predictor from the input to produce a residual Mozilla Corporation 2 Integrating PVQ into AV1 ● Introduction of a transformed predictors both in encoder and decoder – Because PVQ references the predictor in the transform domain, instead of using a pixel-domain residual as in traditional scalar quantization ● Activity masking, the major benefit of PVQ, is not enabled yet – Encoding RDO is solely based on PSNR Mozilla Corporation 3 Traditional Architecture Input X residue Subtraction Transform T signal R predictor P + decoded X Inverse Inverse Scalar decoded R Transform Quantizer Quantizer Coefficient bitstream of Coder coded T(X) Mozilla Corporation 4 AV1 with PVQ Input X Transform T T(X) PVQ Quantizer PVQ Coefficient predictor P Transform T T(X) Coder PVQ Inverse Quantizer Inverse dequantized bitstream of decoded X Transform T(X) coded T(X) Mozilla Corporation 5 Coding Gain Change Metric AV1 --> AV1 with PVQ PSNR Y -0.17 PSNR-HVS 0.27 SSIM 0.93 MS-SSIM 0.14 CIEDE2000 -0.28 ● For the IETF test sequence set, "objective-1-fast". ● IETF and AOM for high latency encoding options are used. Mozilla Corporation 6 Speed ● Increase in total encoding time due to PVQ's search for best codepoint – PVQ's time complexity is close to O(n*n) for n coefficients, while scalar quantization has O(n) ● Compared to Daala, the search space for a RDO decision in AV1 is far larger – For the 1st frame of grandma_qcif (176x144) in intra frame mode, Daala calls PVQ 3843 times, while AV1 calls 632,520 times, that is ~165x. -
Efficient Multi-Codec Support for OTT Services: HEVC/H.265 And/Or AV1?
Efficient Multi-Codec Support for OTT Services: HEVC/H.265 and/or AV1? Christian Timmerer†,‡, Martin Smole‡, and Christopher Mueller‡ ‡Bitmovin Inc., †Alpen-Adria-Universität San Francisco, CA, USA and Klagenfurt, Austria, EU ‡{firstname.lastname}@bitmovin.com, †{firstname.lastname}@itec.aau.at Abstract – The success of HTTP adaptive streaming is un- multiple versions (e.g., different resolutions and bitrates) and disputed and technical standards begin to converge to com- each version is divided into predefined pieces of a few sec- mon formats reducing market fragmentation. However, other onds (typically 2-10s). A client first receives a manifest de- obstacles appear in form of multiple video codecs to be sup- scribing the available content on a server, and then, the client ported in the future, which calls for an efficient multi-codec requests pieces based on its context (e.g., observed available support for over-the-top services. In this paper, we review the bandwidth, buffer status, decoding capabilities). Thus, it is state of the art of HTTP adaptive streaming formats with re- able to adapt the media presentation in a dynamic, adaptive spect to new services and video codecs from a deployment way. perspective. Our findings reveal that multi-codec support is The existing different formats use slightly different ter- inevitable for a successful deployment of today's and future minology. Adopting DASH terminology, the versions are re- services and applications. ferred to as representations and pieces are called segments, which we will use henceforth. The major differences between INTRODUCTION these formats are shown in Table 1. We note a strong differ- entiation in the manifest format and it is expected that both Today's over-the-top (OTT) services account for more than MPEG's media presentation description (MPD) and HLS's 70 percent of the internet traffic and this number is expected playlist (m3u8) will coexist at least for some time.