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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. -
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 -
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. -
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 ..................................................................................................................................... -
Arxiv:2002.01657V1 [Eess.IV] 5 Feb 2020 Port Lossless Model to Compress Images Lossless
LEARNED LOSSLESS IMAGE COMPRESSION WITH A HYPERPRIOR AND DISCRETIZED GAUSSIAN MIXTURE LIKELIHOODS Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan. ABSTRACT effectively in [12, 13, 14]. Some methods decorrelate each Lossless image compression is an important task in the field channel of latent codes and apply deep residual learning to of multimedia communication. Traditional image codecs improve the performance as [15, 16, 17]. However, deep typically support lossless mode, such as WebP, JPEG2000, learning based lossless compression has rarely discussed. FLIF. Recently, deep learning based approaches have started One related work is L3C [18] to propose a hierarchical archi- to show the potential at this point. HyperPrior is an effective tecture with 3 scales to compress images lossless. technique proposed for lossy image compression. This paper In this paper, we propose a learned lossless image com- generalizes the hyperprior from lossy model to lossless com- pression using a hyperprior and discretized Gaussian mixture pression, and proposes a L2-norm term into the loss function likelihoods. Our contributions mainly consist of two aspects. to speed up training procedure. Besides, this paper also in- First, we generalize the hyperprior from lossy model to loss- vestigated different parameterized models for latent codes, less compression model, and propose a loss function with L2- and propose to use Gaussian mixture likelihoods to achieve norm for lossless compression to speed up training. Second, adaptive and flexible context models. Experimental results we investigate four parameterized distributions and propose validate our method can outperform existing deep learning to use Gaussian mixture likelihoods for the context model. -
Fuzzing and Analysis of AV1 Multimedia Codec
Rochester Institute of Technology B. Thomas Golisano College of Computing and Information Sciences Master of Science in Computing Security Fuzzing and Analysis of AV1 Multimedia Codec Monika McKeown ([email protected]) July 26, 2018 Abstract AV1 is a new competitive video codec being developed by a consortium of companies under the Alliance for Open Media (AOM) organization. Their goal is to create a competitive open source codec that allows royalty free usage for all its users. This project aims to investigate the reference implementations for this codec and attempt to analyze the source code for potential issues or vulnerabilities. To do so, a series of fuzzing and static analysis tools were used in the reference encoder and decoder, with automation in place to generate encoded files on a daily basis using the latest version of the library. From these reference files, over 100 million fuzzed versions were created and executed with the decoder providing over 100 unique crashes according to the tools utilized. From these fuzzed files and the static analysis results a number of potential vulnerabilities were discovered. An analysis of these revealed that some were theoretical only, while others have practical concerns and exploitability. The vast majority of these were related to memory management errors, with the worst case causing stack overflows and invalid memory reads. Monika McKeown 1 Contents 1 Introduction 5 2 Literature Review 5 2.1 Video Codec Competitive Environment..............................5 2.2 AV1 Reference Implementation Architecture............................6 2.2.1 AV1 Bitstream Format....................................7 2.3 Techniques..............................................7 2.4 Testing.................................................8 2.5 Common Vulnerabilities...................................... -
Performance Comparison of AV1, JEM, VP9, and HEVC Encoders
To be published in Applications of Digital Image Processing XL, edited by Andrew G. Tescher, Proceedings of SPIE Vol. 10396 © 2017 SPIE Performance Comparison of AV1, JEM, VP9, and HEVC Encoders Dan Grois, Tung Nguyen, and Detlev Marpe Video Coding & Analytics Department Fraunhofer Institute for Telecommunications – Heinrich Hertz Institute, Berlin, Germany [email protected],{tung.nguyen,detlev.marpe}@hhi.fraunhofer.de ABSTRACT This work presents a performance evaluation of the current status of two distinct lines of development in future video coding technology: the so-called AV1 video codec of the industry-driven Alliance for Open Media (AOM) and the Joint Exploration Test Model (JEM), as developed and studied by the Joint Video Exploration Team (JVET) on Future Video Coding of ITU-T VCEG and ISO/IEC MPEG. As a reference, this study also includes reference encoders of the respective starting points of development, as given by the first encoder release of AV1/VP9 for the AOM-driven technology, and the HM reference encoder of the HEVC standard for the JVET activities. For a large variety of video sources ranging from UHD over HD to 360° content, the compression capability of the different video coding technology has been evaluated by using a Random Access setting along with the JVET common test conditions. As an outcome of this study, it was observed that the latest AV1 release achieved average bit-rate savings of ~17% relative to VP9 at the expense of a factor of ~117 in encoder run time. On the other hand, the latest JEM release provides an average bit-rate saving of ~30% relative to HM with a factor of ~10.5 in encoder run time. -
Video Compression Optimized for Racing Drones
Video compression optimized for racing drones Henrik Theolin Computer Science and Engineering, master's level 2018 Luleå University of Technology Department of Computer Science, Electrical and Space Engineering Video compression optimized for racing drones November 10, 2018 Preface To my wife and son always! Without you I'd never try to become smarter. Thanks to my supervisor Staffan Johansson at Neava for providing room, tools and the guidance needed to perform this thesis. To my examiner Rickard Nilsson for helping me focus on the task and reminding me of the time-limit to complete the report. i of ii Video compression optimized for racing drones November 10, 2018 Abstract This thesis is a report on the findings of different video coding tech- niques and their suitability for a low powered lightweight system mounted on a racing drone. Low latency, high consistency and a robust video stream is of the utmost importance. The literature consists of multiple comparisons and reports on the efficiency for the most commonly used video compression algorithms. These reports and findings are mainly not used on a low latency system but are testing in a laboratory environment with settings unusable for a real-time system. The literature that deals with low latency video streaming and network instability shows that only a limited set of each compression algorithms are available to ensure low complexity and no added delay to the coding process. The findings re- sulted in that AVC/H.264 was the most suited compression algorithm and more precise the x264 implementation was the most optimized to be able to perform well on the low powered system. -
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RULING CODECS of VIDEO COMPRESSION and ITS FUTURE 1Dr.Manju.V.C, 2Apsara and 3Annapoorna
International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org Email: [email protected] Volume 8, Issue 7, July 2019 ISSN 2319 - 4847 RULING CODECS OF VIDEO COMPRESSION AND ITS FUTURE 1Dr.Manju.v.C, 2Apsara and 3Annapoorna 1Professor,KSIT 2Under-Gradute, Telecommunication Engineering, KSIT 3Under-Gradute, Telecommunication Engineering, KSIT Abstract Development of economical video codecs for low bitrate video transmission with higher compression potency has been a vigorous space of analysis over past years and varied techniques are projected to fulfill this need. Video Compression is a term accustomed outline a technique for reducing the information accustomed cipher digital video content. This reduction in knowledge interprets to edges like smaller storage needs and lower transmission information measure needs, for a clip of video content. In this paper, we survey different video codec standards and also discuss about the challenges in VP9 codec Keywords: Codecs, HEVC, VP9, AV1 1. INTRODUCTION Storing video requires a lot of storage space. To make this task manageable, video compression technology was developed to compress video, also known as a codec. Video coding techniques provide economic solutions to represent video data in a more compact and stout way so that the storage and transmission of video will be completed in less value in less cost in terms of bandwidth, size and power consumption. This technology (video compression) reduces redundancies in spatial and temporal directions. Spatial reduction physically reduces the size of the video data by selectively discarding up to a fourth or more of unneeded parts of the original data in a frame [1].