Why “Not- Compressing” Simply Doesn't Make Sens ?
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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 -
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+) -
Press Release of 132Nd MPEG Meeting
ISO/IEC JTC 1/SC 29/AG 03 N00007 ISO/IEC JTC 1/SC 29/AG 03 MPEG Liaison and Communication Convenorship: KATS (Korea, Republic of) Document type: Press Release Title: Press Release of 132nd MPEG Meeting Status: Approved Date of document: 2020-10-16 Source: Convenor Expected action: INFO No. of pages: 8 (incl. cover page) Email of convenor: [email protected] Committee URL: https://isotc.iso.org/livelink/livelink/open/jtc1sc29ag3 INTERNATIONAL ORGANIZATION FOR STANDARDIZATION ORGANISATION INTERNATIONALE DE NORMALISATION ISO/IEC JTC 1/SC 29/AG 03 MPEG LIAISON AND COMMUNICATION ISO/IEC JTC 1/SC 29/AG 03 N00007 Online Meeting – October 2020 Source: Convenor of ISO/IEC JTC 1/SC 29/AG 03 Status: Approved by AG 03 Subject: Press Release of 132nd MPEG Meeting Date: 16 October 2020 Serial Number 19862 MPEG Continues to Progress – First Meeting with the New Structure The 132nd MPEG meeting was held online, 12 – 16 October 2020 Table of Contents: MPEG CONTINUES TO PROGRESS – FIRST MEETING WITH THE NEW STRUCTURE .......................................... 3 VERSATILE VIDEO CODING (VVC) ULTRA-HD VERIFICATION TEST COMPLETED AND CONFORMANCE AND REFERENCE SOFTWARE STANDARDS REACH THEIR FIRST MILESTONE ........................................................... 3 MPEG COMPLETES GEOMETRY-BASED POINT CLOUD COMPRESSION STANDARD ......................................... 4 MPEG EVALUATES EXTENSIONS AND IMPROVEMENTS TO MPEG-G AND ANNOUNCES A CALL FOR EVIDENCE ON NEW ADVANCED GENOMICS FEATURES AND TECHNOLOGIES ................................................................ 4 MPEG ISSUES DRAFT CALL FOR PROPOSALS ON THE CODED REPRESENTATION OF HAPTICS .......................... 5 MPEG EVALUATES RESPONSES TO MPEG IPR SMART CONTRACTS CFP ......................................................... 6 MPEG COMPLETES STANDARD ON HARMONIZATION OF DASH AND CMAF .................................................. 6 MPEG COMPLETES 2ND EDITION OF THE OMNIDIRECTIONAL MEDIA FORMAT .............................................. -
Optimized Bitrate Ladders for Adaptive Video Streaming with Deep Reinforcement Learning
Optimized Bitrate Ladders for Adaptive Video Streaming with Deep Reinforcement Learning ∗ Tianchi Huang1, Lifeng Sun1,2,3 1Dept. of CS & Tech., 2BNRist, Tsinghua University. 3Key Laboratory of Pervasive Computing, China ABSTRACT Transcoding Online Stage Video Quality Stage In the adaptive video streaming scenario, videos are pre-chunked Storage Cost and pre-encoded according to a set of resolution-bitrate/quality Deploy pairs on the server-side, namely bitrate ladder. Hence, we pro- … … pose DeepLadder, which adopts state-of-the-art deep reinforcement learning (DRL) method to optimize the bitrate ladder by consid- Transcoding Server ering video content features, current network capacities, as well Raw Videos Video Chunks NN-based as the storage cost. Experimental results on both Constant Bi- Decison trate (CBR) and Variable Bitrate (VBR)-encoded videos demonstrate Network & ABR Status Feedback that DeepLadder significantly improvements on average video qual- ity, bandwidth utilization, and storage overhead in comparison to Figure 1: An Overview of DeepLadder’s System. We leverage prior work. a NN-based decision model for constructing the proper bi- trate ladders, and transcode the video according to the as- CCS CONCEPTS signed settings. • Information systems → Multimedia streaming; • Computing and solve the problem mathematically. In this poster, we propose methodologies → Neural networks; DeepLadder, a per-chunk video transcoding system. Technically, we set video contents, current network traffic distributions, past KEYWORDS actions as the state, and utilize a neural network (NN) to deter- Bitrate Ladder Optimization, Deep Reinforcement Learning. mine the proper action for each resolution autoregressively. Unlike the traditional bitrate ladder method that outputs all candidates ACM Reference Format: at one step, we model the optimization process as a Markov Deci- Tianchi Huang, Lifeng Sun. -
Cube Encoder and Decoder Reference Guide
CUBE ENCODER AND DECODER REFERENCE GUIDE © 2018 Teradek, LLC. All Rights Reserved. TABLE OF CONTENTS 1. Introduction ................................................................................ 3 Support Resources ........................................................ 3 Disclaimer ......................................................................... 3 Warning ............................................................................. 3 HEVC Products ............................................................... 3 HEVC Content ................................................................. 3 Physical Properties ........................................................ 4 2. Getting Started .......................................................................... 5 Power Your Device ......................................................... 5 Connect to a Network .................................................. 6 Choose Your Application .............................................. 7 Choose a Stream Mode ............................................... 9 3. Encoder Configuration ..........................................................10 Video/Audio Input .......................................................12 Color Management ......................................................13 Encoder ...........................................................................14 Network Interfaces .....................................................15 Cloud Services ..............................................................17 -
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. -
Bitmovin's “Video Developer Report 2018,”
MPEG MPEG VAST VAST HLS HLS DASH DASH H.264 H.264 AV1 AV1 HLS NATIVE NATIVE CMAF CMAF RTMP RTMP VP9 VP9 ANDROID ANDROID ROKU ROKU HTML5 HTML5 Video Developer MPEG VAST4.0 MPEG VAST4.0 HLS HLS DASH DASH Report 2018 H.264 H.264 AV1 AV1 NATIVE NATIVE CMAF CMAF ROKU RTMP ROKU RTMP VAST4.0 VAST4.0 VP9 VP9 ANDROID ANDROID HTML5 HTML5 DRM DRM MPEG MPEG VAST VAST DASH DASH AV1 HLS AV1 HLS NATIVE NATIVE H.264 H.264 CMAF CMAF RTMP RTMP VP9 VP9 ANDROID ANDROID ROKU ROKU MPEG VAST4.0 MPEG VAST4.0 HLS HLS DASH DASH H.264 H.264 AV1 AV1 NATIVE NATIVE CMAF CMAF ROKU ROKU Welcome to the 2018 Video Developer Report! First and foremost, I’d like to thank everyone for making the 2018 Video Developer Report possible! In its second year the report is wider both in scope and reach. With 456 survey submissions from over 67 countries, the report aims to provide a snapshot into the state of video technology in 2018, as well as a vision into what will be important in the next 12 months. This report would not be possible without the great support and participation of the video developer community. Thank you for your dedication to figuring it out. To making streaming video work despite the challenges of limited bandwidth and a fragmented consumer device landscape. We hope this report provides you with insights into what your peers are working on and the pain points that we are all experiencing. We have already learned a lot and are looking forward to the 2019 Video Developer Survey a year from now! Best Regards, StefanStefan Lederer Lederer CEO, Bitmovin Page 1 Key findings In 2018 H.264/AVC dominates video codec usage globally, used by 92% of developers in the survey. -
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter Prediction using Binary classification techniques A graduate project submitted in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering by Alex Kit Romero May 2020 The graduate project of Alex Kit Romero is approved: ____________________________________ ____________ Dr. Katya Mkrtchyan Date ____________________________________ ____________ Dr. Kyle Dewey Date ____________________________________ ____________ Dr. John J. Noga, Chair Date California State University, Northridge ii Dedication This project is dedicated to all of the Computer Science professors that I have come in contact with other the years who have inspired and encouraged me to pursue a career in computer science. The words and wisdom of these professors are what pushed me to try harder and accomplish more than I ever thought possible. I would like to give a big thanks to the open source community and my fellow cohort of computer science co-workers for always being there with answers to my numerous questions and inquiries. Without their guidance and expertise, I could not have been successful. Lastly, I would like to thank my friends and family who have supported and uplifted me throughout the years. Thank you for believing in me and always telling me to never give up. iii Table of Contents Signature Page ................................................................................................................................ ii Dedication ..................................................................................................................................... -
How to Encode Video for the Future
How to Encode Video for the Future Dror Gill, CTO, Beamr Abstract The quality expectations of viewers paired with the ever-increasing shift to over-the-top (OTT) and mobile video consumption, are driving today’s networks to be more congested with video than ever before. To counter this congestion, this paper will cover advanced techniques for applying content-adaptive encoding and optimization methods to video workflows while lowering the bitrate of encoded video without compromising quality. Intended Audience: Business decision makers Video encoding engineers To learn more about optimized content-adaptive encoding, email [email protected] ©Beamr Imaging Ltd. 2017 | beamr.com Table of contents 3 Encoding for the future. 3 The trade off between bitrate and quality. 3 Legacy approaches to encoding your video content. 3 What is constant bitrate (CBR) encoding? 4 What about variable bitrate encoding? 4 Encoding content with constant rate factor encoding. 4 Capped content rate factor encoding for high complexity scenes. 4 Encoding content for the future. 5 Manually encoding content by title. 5 Manually encoding content by the category. 6 Content-adaptive encoding by the title and chunk. 6 Content-adaptive encoding using neural networks. 7 Closed loop content-adaptive encoding by the frame. 9 How should you be encoding your content? 10 References ©Beamr Imaging Ltd. 2017 | beamr.com Encoding for the future. how it will impact the file size and perceived visual quality of the video. The standard method of encoding video for delivery over the Internet utilizes a pre-set group of resolutions The rate control algorithm adjusts encoder parameters and bitrates known as adaptive bitrate (ABR) sets. -
Download the Inspector Product Sheet (Pdf)
INSPECTOR Because your lab only has so many people... SSIMPLUS VOD Monitor Inspector is the only video quality measurement software with the algorithm trusted by Hollywood to determine the best possible configuration for R&D groups, engineers and architects who set up VOD encoding and processing workflows or make purchasing recommendations. Video professionals can evaluate more encoders and transcoders with the fastest and most comprehensive solution in the business. From the start of your workflow to delivering to consumer endpoints, VOD Monitor Inspector is here to help ensure every step along the way works flawlessly. Easy-to-use tools provide: A/B testing for encoding configurations and purchasing decisions Sandbox environment for encoder or transcoder output troubleshooting “The SSIMPLUS score developed Creation of custom templates to identify best practices for specific content libraries by SSIMWAVE represents a Configurable automation to save time and eliminate manual QA/QC generational breakthrough in Side-by-side visual inspector to subjectively assess degradations the video industry.” Perceptual quality maps that provide pixel level graphic visualization –The Television Academy of content impairments Allows you to optimize network performance and improve quality Our Emmy Award-winning SSIMPLUS™ score mimics the accuracy of 100,000 human eyes. Know the score when it YOU CAN HOW OUR SEE THE SOFTWARE SEES DEGRADATION THE DEGRADATION comes to video quality NARROW IT DOWN TO THE The SSIMPLUS score is the most accurate measurement PIXEL LEVEL representing how end-viewers perceive video quality. Our score can tell exactly where video quality degrades. 18 34 59 72 87 10 20 30 40 50 60 70 80 90 100 BAD POOR FAIR GOOD EXCELLENT Helping your workflow, work SSIMPLUS VOD Monitor Inspector helps ensure your video infrastructure is not negatively impacting content anywhere in your workflow.