Constant Quality Mode 4K Video Comparison Using AV1 Reference Tool
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Game of Thrones
2012 ISSN 1433-2620 > B 43362 >> 16. Jahrgang >>> www.digitalproduction.com Deutschland € 14,95 Published by Österreich € 17,– 5 Schweiz sfr 23,– 05|12 MAGAZIN FÜR DIGITALE MEDIENPRODUKTION SEPTEMBER | OKTOBER 05|12 Game of Thrones | ParaNorman | Cinema 4D R14 | Maya 2013 | Element | Cinema 4D R14 Maya | ParaNorman 3DThrones | | SpeedGrade of Game Sony NEX Sony | SynthEyes | HP Z1 & HP Z820 | Tears of Steel | HP Z1 & Z820 Tears | SynthEyes Pixomondo erobert Westeros Game of Thrones Cinema 4D R14 Element 3D Maya 2013 Seit wann kann C4D Animation für Adobes Workflow-Winner oder sculpten? After Effects Pflicht-Update? DDP1205_001-001_U1_TitelP1205_001-001_U1_Titel 1 009.08.20129.08.2012 008:28:148:28:14 AKTUELL FILM & VFX 3D & ANIMATION INTERACTIVE INDUSTRIE DIGITAL ART SCIENCE & EDUCATION SERVICE Alle Bilder: (CC) Blender Foundation | mango.blender.org Matte Painting – WIP einer Matte für die Kuppel, in der die Zeitreise passiert. Tears of Steel – Film als Open Source Ende September soll das neue Open Movie „Tears of Steel“ (Codename: Project Mango) der Blender Foundation fertig gestellt werden. Es handelt sich dabei um einen Film zum Anfassen, zum Untersuchen und zum Studieren. Bis ins kleinste Detail werden die Quellen des Films offengelegt. Für den VFX-Interessierten eine wahre Fundgru- be. Gleichzeitig dient der Film dazu, die Blender-basierte Produktions-Pipeline zu erkunden, zu verbessern und fit zu machen für VFX. DP wagt einen Blick unter die (offene) Haube. von Gottfried Hofmann 104 WWW.DIGITALPRODUCTION.COM DDP1205_104-109_TearsSteelP1205_104-109_TearsSteel 110404 009.08.20129.08.2012 009:56:149:56:14 AUSGABE 05|12 BLENDER | OPEN MOVIE Open Source Footage Da „Tears of Steel“ Open Source ist, werden Sie nach der Fertigstellung das gesamte Material herunterladen können unter http://mango.blender.org/. -
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 -
W4: OBJECTIVE QUALITY METRICS 2D/3D Jan Ozer [email protected] 276-235-8542 @Janozer Course Overview
W4: OBJECTIVE QUALITY METRICS 2D/3D Jan Ozer www.streaminglearningcenter.com [email protected] 276-235-8542 @janozer Course Overview • Section 1: Validating metrics • Section 2: Comparing metrics • Section 3: Computing metrics • Section 4: Applying metrics • Section 5: Using metrics • Section 6: 3D metrics Section 1: Validating Objective Quality Metrics • What are objective quality metrics? • How accurate are they? • How are they used? • What are the subjective alternatives? What Are Objective Quality Metrics • Mathematical formulas that (attempt to) predict how human eyes would rate the videos • Faster and less expensive than subjective tests • Automatable • Examples • Video Multimethod Assessment Fusion (VMAF) • SSIMPLUS • Peak Signal to Noise Ratio (PSNR) • Structural Similarity Index (SSIM) Measure of Quality Metric • Role of objective metrics is to predict subjective scores • Correlation with Human MOS (mean opinion score) • Perfect score - objective MOS matched actual subjective tests • Perfect diagonal line Correlation with Subjective - VMAF VMAF PSNR Correlation with Subjective - SSIMPLUS PSNR SSIMPLUS SSIMPLUS How Are They Used • Netflix • Per-title encoding • Choosing optimal data rate/rez combination • Facebook • Comparing AV1, x265, and VP9 • Researchers • BBC comparing AV1, VVC, HEVC • My practice • Compare codecs and encoders • Build encoding ladders • Make critical configuration decisions Day to Day Uses • Optimize encoding parameters for cost and quality • Configure encoding ladder • Compare codecs and encoders • Evaluate -
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. -
Introducing Basic Principles of Haptic Cinematography and Editing
Eurographics Workshop on Intelligent Cinematography and Editing (2016) M. Christie, Q. Galvane, A. Jhala, and R. Ronfard (Editors) Introducing Basic Principles of Haptic Cinematography and Editing Philippe Guillotely1, Fabien Danieau1, Julien Fleureau1, and Ines Rouxel2 1Technicolor, Cesson-Sévigné, France. 2ESRA, Rennes, France. Abstract Adding the sense of touch to hearing and seeing would be necessary for a true immersive experience. This is the promise of the growing "4D-cinema" based on motion platforms and others sensory effects (water spray, wind, scent, etc.). Touch provides a new dimension for filmmakers and leads to a new creative area, the haptic cinematography. However design rules are required to use this sensorial modality in the right way for increasing the user experience. This paper addresses this issue, by introducing principles of haptic cinematography editing. The proposed elements are based on early feedback from different creative works performed by the authors (including a student in cinema arts), anticipating the role of haptographers, the experts on haptic content creation. Three full short movies have been augmented with haptic feedback and tested by numerous users, in order to provide the inputs for this introductory paper. Categories and Subject Descriptors (according to ACM CCS): 1. Tactile: the perception of vibrations, pressure and temperature H.5.2 [HCI]: User Interfaces—Haptic I/O through the skin; 2. Kinesthetic: the perception of positions and movements of limbs and forces from spindles and tendons; 1. Introduction 3. Proprioception: the perception of position and posture of the Today only two senses are stimulated when being in a movie the- body in space. -
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. -
Ulrich Kaiser Die Einheiten Dieses Openbooks Werden Mittelfristig Auch Auf Elmu ( Bereitge- Stellt Werden
Ulrich Kaiser Die Einheiten dieses OpenBooks werden mittelfristig auch auf elmu (https://elmu.online) bereitge- stellt werden. Die Website elmu ist eine von dem gemeinnützigen Verein ELMU Education e.V. getra- gene Wikipedia zur Musik. Sie sind herzlich dazu eingeladen, in Zukun Verbesse rungen und Aktualisierungen meiner OpenBooks mitzugestalten! Zu diesem OpenBook finden Sie auch Materialien auf musikanalyse.net: • Filmanalyse (Terminologie): http://musikanalyse.net/tutorials/filmanalyse-terminologie/ • Film Sample-Library (CC0): http://musikanalyse.net/tutorials/film-sample-library-cc0/ Meine Open Educational Resources (OER) sind kostenlos erhältlich. Auch öffentliche Auf- führungen meiner Kompositionen und Arrangements sind ohne Entgelt möglich, weil ich durch keine Verwertungsgesellschaft vertreten werde. Gleichwohl kosten Open Educatio- nal Resources Geld, nur werden diese Kosten nicht von Ihnen, sondern von anderen ge- tragen (z.B. von mir in Form meiner Ar beits zeit, den Kosten für die Domains und den Server, die Pflege der Webseiten usw.). Wenn Sie meine Arbeit wertschätzen und über ei- ne Spende unter stützen möchten, bedanke und freue ich mich: Kontoinhaber: Ulrich Kaiser / Institut: ING / Verwendungszweck: OER IBAN: DE425001 0517 5411 1667 49 / BIC: INGDDEFF 1. Auflage: Karlsfeld 2020 Autor: Ulrich Kaiser Umschlag, Layout und Satz Ulrich Kaiser erstellt in Scribus 1.5.5 Dieses Werk wird unter CC BY-SA 4.0 veröffentlicht: http://creativecommons.org/licenses/by-sa/4.0/legalcode Für die Covergestaltung (U1 und U4) wurden verwendet: -
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