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Video Codec Requirements and Evaluation Methodology
Video Codec Requirements 47pt 30pt and Evaluation Methodology Color::white : LT Medium Font to be used by customers and : Arial www.huawei.com draft-filippov-netvc-requirements-01 Alexey Filippov, Huawei Technologies 35pt Contents Font to be used by customers and partners : • An overview of applications • Requirements 18pt • Evaluation methodology Font to be used by customers • Conclusions and partners : Slide 2 Page 2 35pt Applications Font to be used by customers and partners : • Internet Protocol Television (IPTV) • Video conferencing 18pt • Video sharing Font to be used by customers • Screencasting and partners : • Game streaming • Video monitoring / surveillance Slide 3 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : • Basic requirements: . Random access to pictures 18pt Random Access Period (RAP) should be kept small enough (approximately, 1-15 seconds); Font to be used by customers . Temporal (frame-rate) scalability; and partners : . Error robustness • Optional requirements: . resolution and quality (SNR) scalability Slide 4 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : Resolution Frame-rate, fps Picture access mode 2160p (4K),3840x2160 60 RA 18pt 1080p, 1920x1080 24, 50, 60 RA 1080i, 1920x1080 30 (60 fields per second) RA Font to be used by customers and partners : 720p, 1280x720 50, 60 RA 576p (EDTV), 720x576 25, 50 RA 576i (SDTV), 720x576 25, 30 RA 480p (EDTV), 720x480 50, 60 RA 480i (SDTV), 720x480 25, 30 RA Slide 5 35pt Video conferencing Font to be used by customers and partners : • Basic requirements: . Delay should be kept as low as possible 18pt The preferable and maximum delay values should be less than 100 ms and 350 ms, respectively Font to be used by customers . -
(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. -
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 -
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. -
Why “Not- Compressing” Simply Doesn't Make Sens ?
WHY “NOT- COMPRESSING” SIMPLY DOESN’T MAKE SENS ? Confidential IMAGES AND VIDEOS ARE LIKE SPONGES It seems to be a solid. But what happens when you squeeze it? It gets smaller! Why? If you look closely at the sponge, you will see that it has lots of holes in it. The sponge is made up of a mixture of solid and gas. When you squeeze it, the solid part changes it shape, but stays the same size. The gas in the holes gets smaller, so the entire sponge takes up less space. Confidential 2 IMAGES AND VIDEOS ARE LIKE SPONGES There is a lot of data that are not bringing any information to our human eyes, and we can remove it. It does not make sense to transport, store uncompressed images/videos. It is adding data that have an undeniable cost and that are not bringing any additional valuable information to the viewers. Confidential 3 A 4K TV SHOW WITHOUT ANY CODEC ▪ 60 MINUTES STORAGE: 4478.85 GB ▪ STREAMING: 9.953 Gbit per sec Confidential 4 IMAGES AND VIDEOS ARE LIKE SPONGES Remove Remove Remove Remove Temporal redundancy Spatial redundancy Visual redundancy Coding redundancy (interframe prediction,..) (transforms,..) (quantization,…) (entropy coding,…) Confidential 5 WHAT IS A CODEC? Short for COder & DECoder “A codec is a device or computer program for encoding or decoding a digital data stream or signal.” Note : ▪ It does not (necessarily) define quality ▪ It does not define transport / container format method. Confidential 6 WHAT IS A CODEC? Quality, Latency, Complexity, Bandwidth depend on how the actual algorithms are processing the content. -
Video Compression and Communications: from Basics to H.261, H.263, H.264, MPEG2, MPEG4 for DVB and HSDPA-Style Adaptive Turbo-Transceivers
Video Compression and Communications: From Basics to H.261, H.263, H.264, MPEG2, MPEG4 for DVB and HSDPA-Style Adaptive Turbo-Transceivers by c L. Hanzo, P.J. Cherriman, J. Streit Department of Electronics and Computer Science, University of Southampton, UK About the Authors Lajos Hanzo (http://www-mobile.ecs.soton.ac.uk) FREng, FIEEE, FIET, DSc received his degree in electronics in 1976 and his doctorate in 1983. During his 30-year career in telecommunications he has held various research and academic posts in Hungary, Germany and the UK. Since 1986 he has been with the School of Electronics and Computer Science, University of Southampton, UK, where he holds the chair in telecom- munications. He has co-authored 15 books on mobile radio communica- tions totalling in excess of 10 000, published about 700 research papers, acted as TPC Chair of IEEE conferences, presented keynote lectures and been awarded a number of distinctions. Currently he is directing an academic research team, working on a range of research projects in the field of wireless multimedia communications sponsored by industry, the Engineering and Physical Sciences Research Council (EPSRC) UK, the European IST Programme and the Mobile Virtual Centre of Excellence (VCE), UK. He is an enthusiastic supporter of industrial and academic liaison and he offers a range of industrial courses. He is also an IEEE Distinguished Lecturer of both the Communications Society and the Vehicular Technology Society (VTS). Since 2005 he has been a Governer of the VTS. For further information on research in progress and associated publications please refer to http://www-mobile.ecs.soton.ac.uk Peter J. -
Comparison of Video Compression Standards
International Journal of Computer and Electrical Engineering, Vol. 5, No. 6, December 2013 Comparison of Video Compression Standards S. Ponlatha and R. S. Sabeenian display digital pictures. Each pixel is thus represented by Abstract—In order to ensure compatibility among video one R, G, and B components. The 2D array of pixels that codecs from different manufacturers and applications and to constitutes a picture is actually three 2D arrays with one simplify the development of new applications, intensive efforts array for each of the RGB components. A resolution of 8 have been undertaken in recent years to define digital video bits per component is usually sufficient for typical consumer standards Over the past decades, digital video compression applications. technologies have become an integral part of the way we create, communicate and consume visual information. Digital A. The Need for Compression video communication can be found today in many application sceneries such as broadcast services over satellite and Fortunately, digital video has significant redundancies terrestrial channels, digital video storage, wires and wireless and eliminating or reducing those redundancies results in conversational services and etc. The data quantity is very large compression. Video compression can be lossy or loss less. for the digital video and the memory of the storage devices and Loss less video compression reproduces identical video the bandwidth of the transmission channel are not infinite, so after de-compression. We primarily consider lossy it is not practical for us to store the full digital video without compression that yields perceptually equivalent, but not processing. For instance, we have a 720 x 480 pixels per frame,30 frames per second, total 90 minutes full color video, identical video compared to the uncompressed source. -
A Dataflow Description of ACC-JPEG Codec
A Dataflow Description of ACC-JPEG Codec Khaled Jerbi1,2, Tarek Ouni1 and Mohamed Abid1 1CES Lab, National Engineering School of Sfax, Sfax, Tunisia 2IETR/INSA, UMR CNRS 6164, F-35043 Rennes, France Keywords: Video Compression, Accordeon, Dataflow, MPEG RVC. Abstract: Video codec standards evolution raises two major problems. The first one is the design complexity which makes very difficult the video coders implementation. The second is the computing capability demanding which requires complex and advanced architectures. To decline the first problem, MPEG normalized the Re- configurable Video Coding (RVC) standard which allows the reutilization of some generic image processing modules for advanced video coders. However, the second problem still remains unsolved. Actually, technol- ogy development becomes incapable to answer the current standards algorithmic increasing complexity. In this paper, we propose an efficient solution for the two problems by applying the RVC methodology and its associated tools on a new video coding model called Accordion based video coding. The main advantage of this video coding model consists in its capacity of providing high compression efficiency with low complexity which is able to resolve the second video coding problem. 1 INTRODUCTION nology from all existing MPEG video past standards (i.e. MPEG- 2, MPEG- 4, etc. ). CAL is used to pro- During most of two decades, MPEG has produced vide the reference software for all coding tools of the several video coding standards such as MPEG-2, entire library. The originality of this paper is the ap- MPEG-4, AVC and SVC. However, the past mono- plication of the CAL and its associated tools on a new lithic specification of such standards (usually in the video coding model called Accordion based video form of C/C++ programs) lacks flexibility. -
Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies
Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies Yanyuan Qin, Ph.D. University of Connecticut, 2021 ABSTRACT Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge. First, the design of an ABR scheme needs to balance conflicting Quality of Experience (QoE) metrics such as video quality, quality changes, stalls and startup performance, which is even harder under highly dynamic bandwidth in cellular network. Second, streaming providers have been moving towards using Variable Bitrate (VBR) encodings for the video content, which introduces new challenges for ABR streaming, whose nature and implications are little understood. Third, mobile video streaming consumes a lot of data. Although many video and network providers currently offer data saving options, the existing practices are suboptimal in QoE and resource usage. Last, when the audio and video arXiv:2104.01104v2 [cs.NI] 14 May 2021 tracks are stored separately, video and audio rate adaptation needs to be dynamically coordinated to achieve good overall streaming experience, which presents interesting challenges while, somewhat surprisingly, has received little attention by the research community. In this dissertation, we tackle each of the above four challenges. Firstly, we design a framework called PIA (PID-control based ABR streaming) that strategically leverages PID control concepts and novel approaches to account for the various requirements of ABR streaming. The evaluation results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with signif- icantly lower bitrate changes and stalls, while incurring very small runtime overhead. -
Comments on `Area and Power Efficient DCT Architecture for Image
Cintra and Bayer EURASIP Journal on Advances in Signal EURASIP Journal on Advances Processing (2017) 2017:50 DOI 10.1186/s13634-017-0486-8 in Signal Processing RESEARCH Open Access Comments on ‘Area and power efficient DCT architecture for image compression’ by Dhandapani and Ramachandran Renato J. Cintra1* and Fábio M. Bayer2 Abstract In [Dhandapani and Ramachandran, “Area and power efficient DCT architecture for image compression”, EURASIP Journal on Advances in Signal Processing 2014, 2014:180] the authors claim to have introduced an approximation for the discrete cosine transform capable of outperforming several well-known approximations in literature in terms of additive complexity. We could not verify the above results and we offer corrections for their work. Keywords: DCT approximations, Low-complexity transforms 1 Introduction 2 Criticisms In a recent paper [1], a low-complexity transformation 2.1 Inverse transformation was introduced, which is claimed to be a good approxima- The authors of [1] claim that inverse transformation T−1 tion to the discrete cosine transform (DCT). We wish to is given by evaluate this claim. ⎡ ⎤ The introduced transformation is given by the following 10001000 ⎢ ⎥ matrix: ⎢ −1100−11 0 0⎥ ⎢ ⎥ ⎢ 00100010⎥ ⎡ ⎤ ⎢ ⎥ 1 ⎢ 00−11 0 0−11⎥ 10000001 · ⎢ ⎥ . ⎢ ⎥ 2 ⎢ 00−11 0 0 1−1 ⎥ ⎢ 11000011⎥ ⎢ ⎥ ⎢ ⎥ ⎢ 001000−10⎥ ⎢ 00100100⎥ ⎣ ⎦ ⎢ ⎥ −11001−10 0 ⎢ 00111100⎥ T = ⎢ ⎥ . 1000−10 0 0 ⎢ 00 1 1−1 −10 0⎥ ⎢ ⎥ ⎢ 00 1 0 0−10 0⎥ ⎣ ⎦ However, simple computation reveal that this is not 110000−1 −1 accurate, being the actual inverse given by: 1000000−1 ⎡ ⎤ 10000001 ⎢ ⎥ We aim at analyzing the above matrix and showing that ⎢ −1100001−1 ⎥ ⎢ ⎥ it does not consist of a meaningful approximation for ⎢ 00100100⎥ ⎢ ⎥ the8-pointDCT.Inthefollowing,weadoptedthesame − 1 ⎢ 00−11 1−10 0⎥ T 1 = · ⎢ ⎥ .