DOCSLIB.ORG

Optimized Bitrate Ladders for Adaptive Video Streaming with Deep Reinforcement Learning

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. 2020. Optimized Bitrate Ladders for Adaptive Video Streaming with Deep Reinforcement Learning. In Annual confer- sion Process (MDP), which can effectively reduce the action space. ence of the ACM Special Interest Group on Data Communication on the DeepLadder employs Dual-PPO [12], the state-of-the-art deep re- applications, technologies, architectures, and protocols for computer com- inforcement learning method, to learn the policy by interacting munication (SIGCOMM ’20 Demos and Posters), August 10–14, 2020, Vir- with the environments without any presumptions, attempting to tual Event, USA. ACM, New York, NY, USA, Article 4,3 pages. https: balance a variety of goals such as maximizing perceptual video //doi.org/10.1145/3405837.3411387 quality, improving bandwidth utilization, and reducing the storage overhead. 1 INTRODUCTION We implement DeepLadder-CBR and DeepLadder-VBR for deal- The Global Internet Phenomena Report COVID-19 Spotlight [9] ing videos with different encoding types (i.e. constant bitrate (CBR)- reveals that at least 51.43% of the video streams are delivered by encoded and variable bitrate(VBR)-encoded videos). Using trace- adaptive video streaming technologies. In such technologies, on driven experiments, first, comparing DeepLadder-CBR with ex- the server-side, video streams are pre-chunked and pre-encoded isting methods under various networks and videos, we observe at various bitrates or quality levels. On the client-side, users often that DeepLadder improves the average quality by 8.49%-14.25%, en- adopt adaptive bitrate (ABR) algorithms to pick the proper bitrates hances the bandwidth utilization by 3.66%-11.68%, and reduces the for chunks to ensure the quality of experience (QoE). We often storage cost by 39.77%-54.84%. Further, the results on DeepLadder- called the server-side encoding settings as bitrate ladder [1]. VBR also illustrate the significant improvements in the average The majority of existing bitrate ladder optimization schemes quality (3.8%-7.7%) and the storage costs (21.26%-44.88%). Finally, often use fixed encoding ladders (known as one-size-fits-all [3]), we demonstrate that DeepLadder is practical and can be deployed or only consider video contents [2] or network conditions [7], into the transcoding framework. Testing results on both CPU and GPU devices indicate that DeepLadder achieves the aforementioned * Corresponding Author. {htc19@mails, sunlf@}tsinghua.edu.cn. Permission to make digital or hard copies of part or all of this work for personal or performance with only 3.4% on extra costs. classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation 2 SYSTEM MECHANISM on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Overview. As illustrated in Figure1, DeepLadder’s basic system SIGCOMM ’20 Demos and Posters, August 10–14, 2020, Virtual Event, USA work-flow is mainly composed of the transcoding stage andthe © 2020 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-8048-5/20/08...$15.00 online stage. In the transcoding stage, we leverage an NN-based https://doi.org/10.1145/3405837.3411387 decision model for constructing the proper bitrate ladders and use Nf-Greedy Pen-Set Nf-Greedy Pen-Set RMPC-Set DeepLadder RMPC-Set DeepLadder Table 1: Real-world implementation of DeepLadder. 75 75 Video Content Inference Transcoding Extra 50 50 Process 25 25 Average Value Average Value Extraction Timespan Time Cost (%) 0 0 Video Quality Bandwidth Storage Video Quality Bandwidth Storage CPU(ms) 439 3 12060 3.67 (VMAF) Utilization (%) Overhead(50KB) (VMAF) Utilization (%) Overhead(50KB) GPU(ms) 94 12 3407 3.11 Figure 2: DeepLadder-CBR vs. existing approaches. ⊲ RobustMPC-Settings [13] (RMPC-Set): an another fixed encoding ladder settings with {300, 700, 1000, 1500, 2000, 3000}kbps. X265 DeepLadder,w=0 X265 DeepLadder,w=0 Furthermore, we select the following baselines for the VBR- Fixed5 DeepLadder,w=1 100 Fixed5 DeepLadder,w=1 100 encoded video scenario: 50 50 ⊲ Fixed-25 [5]: recent studies find that using the CRF value of Average Value Average Value 25 can provide good viewing quality. 0 0 Video Quality Bandwidth Storage Video Quality Bandwidth Storage ⊲ X265 [11]: the default CRF is fixed as 28 [8]. (VQAlog) Utilization (%) Overhead(50KB) (VQAlog) Utilization (%) Overhead(50KB) Trace-driven Evaluation. As shown in Figure2, we test the Figure 3: DeepLadder-VBR vs. basselines. performance of each scheme by the video under the HSDPA and FCC network environment and report the average video qual- ity, bandwidth utilization and storage overhead. We can see that the assigned settings to transcode the video for each chunk. In the DeepLadder outperforms previously proposed methods, with the online stage, the encoded chunks are deployed on the server and improvements on i) average video quality of 8.49% - 12.94% on the wait for fetching. The NN instantly obtains the video quality and HSDPA dataset and 8.78%-14.25% on the FCC dataset; ii) 3.66%- storage from the transcoding stage, as well as periodically receives 11.68% in terms of the bandwidth utilization on HSDPA dataset. the network status feedback from the online stage. Meanwhile, DeepLadder reduces the storage overhead by 39.77%- Details. We mainly categorize the DeepLadder’s state input 54.84% compared with the baselines. Figure3 illustrates the re- into four parts, that is, current network traffic distributions, video sults for the Oboe and HDFS network conditions. As expected, features, past actions, and desired reward weights F. DeepLadder DeepLadder-VBR (F = 0)’s bandwidth utilization is within 8.37%- is allowed to support diversity of coding methods. For the CBR- 15.03% compared with fixed encoding ladder schemes. Moreover, encoded videos, we set the the action 08 = ¹#8−1 ¸ #8 º/2, where DeepLadder-VBR (F = 1) effectively preserves the average video #8 represents the i-th throughput categorized by the bandwidth quality and reduces storage cost by 29.8% on the Oboe traces, 21.26%- histogram method. For the VBR-encoded videos, we set the action 44.88% on the HDFS network. size of DeepLadder-VBR as 20, symbolizing that the videos will be Practical Implementation. We deploy the proposed scheme encoded by Constant Rate Factor (CRF)= 20 − 39. Such settings as a service into the transcoding frameworks and list the time have already covered the best quality for most videos. The reward consumption for each process in Table1, tested on an Intel Core function is listed in Eq.1, given a network condition 퐶, we aim to i7 CPU2.2GHz and an Nvidia 1080Ti GPU. In particular, in the maximize the bandwidth utilization, the average video quality and GPU test, we use the FFmpeg’s integration of NVIDIA Video Codec minimize the storage cost. Here * is the bandwidth utilization, & SDK that enables high-performance hardware-accelerated video is the average video quality and (I is the storage cost, F is a scalar pipelines [10]. We show that DeepLadder (in gray) only consumes which controls the importance of the storage metric. Moreover, 3.11%-3.67% on the extra cost compared with the original system. we use Video Multimethod Assessment Fusion (VMAF) [6] as the quality metrics and measure the quality metric for each video chunk 4 SUMMARY AND FUTURE WORK with all the resolution and encoding bitrate tracks. We present DeepLadder, a novel bitrate ladder construction sys- tem which utilizes state-of-the-art NN-based model to build the Õ Õ Õ proper bitrate encoding profile. DeepLadder’s NN takes current AC = * ¹0C ,퐶º ¸ & ¹0C º − F (I ¹0Cº/C. (1) C C C raw video contents, network traffic capacities, and the storage over- head into the input. We adopt state-of-the-art DRL method to train DeepLadder w.r.t a large corpus of internet network traces and col- 3 PRELIMINARY RESULTS lected videos. Experimental results on both CBR and VBR-encoded Experimental Setup. To better simulate the diversity of real- videos indicate the superiority of DeepLadder over existing ap- world network conditions, we collect over 3,000 network traces, proaches.
Load more

Recommended publications
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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 = · ⎢ ⎥ .
    [Show full text]
  • Lec 15 - HEVC Rate Control
    ECE 5578 Multimedia Communication Lec 15 - HEVC Rate Control Guest Lecturer: Li Li Dept of CSEE, UMKC Office: FH560E, Email: [email protected], Ph: x 2346. http://l.web.umkc.edu/lizhu slides created with WPS Office Linux and EqualX LaTex equation editor Z. Li, ECE 5578 Multimedia Communciation, 2020 Outline 1 Background 2 Related Work 3 Proposed Algorithm 4 Experimental Results 5 Conclusion Rate control introduction Assuming the bandwidth is 2Mbps, the encode parameters should be adjusted to reach the bandwidth Loss of data > 2 2 X Unable to make full use of the < 2 bandwidth How to reach the bandwidth accurately and provide the best video quality ? min D s.t. R £ Rt paras Optimal Rate Control Rate control applications Rate control can be used in various scenarios CBR: Constant Bitrate VBR: Variable Bitrate wire wireless Rate control storage ABR: Average Bitrate Rate distortion optimization Rate distortion optimization min D s.t. R £ Rt Û min D + l(Rt )R paras paras λ determines the optimization target λ Background Rate control Non-normative part of video coding standards Quite widely used: of great importance Considering its importance , rate control algorithm is always included in the reference software of standards MPEG2: TM5 H.263: VM8 H.264: Q-domain rate control HEVC: λ-domain rate control (JCTVC K0103, M0036) Outline 1 Background 2 Related Work 3 Proposed Algorithm 4 Experimental Results 5 Conclusion Related work Q-domain rate control algorithm Yaqin Zhang：Second-order R-Q model The improvement of RMSE in model prediction diminishes as the degree exceeds two.
    [Show full text]
  • Soundstream: an End-To-End Neural Audio Codec Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, Marco Tagliasacchi
    1 SoundStream: An End-to-End Neural Audio Codec Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, Marco Tagliasacchi Abstract—We present SoundStream, a novel neural audio codec that can efficiently compress speech, music and general audio at bitrates normally targeted by speech-tailored codecs. 80 SoundStream relies on a model architecture composed by a fully EVS convolutional encoder/decoder network and a residual vector SoundStream quantizer, which are trained jointly end-to-end. Training lever- ages recent advances in text-to-speech and speech enhancement, SoundStream - scalable which combine adversarial and reconstruction losses to allow Opus the generation of high-quality audio content from quantized 60 embeddings. By training with structured dropout applied to quantizer layers, a single model can operate across variable bitrates from 3 kbps to 18 kbps, with a negligible quality loss EVS when compared with models trained at fixed bitrates. In addition, MUSHRA score the model is amenable to a low latency implementation, which 40 supports streamable inference and runs in real time on a smartphone CPU. In subjective evaluations using audio at 24 kHz Lyra sampling rate, SoundStream at 3 kbps outperforms Opus at 12 kbps and approaches EVS at 9.6 kbps. Moreover, we are able to Opus perform joint compression and enhancement either at the encoder 20 or at the decoder side with no additional latency, which we 3 6 9 12 demonstrate through background noise suppression for speech. Bitrate (kbps) Fig. 1: SoundStream @3 kbps vs. state-of-the-art codecs. I. INTRODUCTION Audio codecs can be partitioned into two broad categories: machine learning models have been successfully applied in the waveform codecs and parametric codecs.
    [Show full text]
  • Bachelor Thesis
    Charles University in Prague Faculty of Mathematics and Physics BACHELOR THESIS Ján Dupej Komprese zvuku Audio Coding Department of Software Engineering Consultant: Mgr. Jan Lánský Specialization: Programming 2009 I would like to thank my consultant, Mgr. Jan Lánský for his invaluable assistance with writing this thesis. Prohlašuji, že jsem svou bakalářskou práci napsal samostatně a výhradně s použitím citovaných pramenů. Souhlasím se zapůjčováním práce. V Praze dne 28.05.2009 Ján Dupej 2 Table of Contents Table of Contents 3 List of Figures 5 List of Tables 5 List of Abbreviations 6 Introduction 8 1. Basic Concepts 10 1.1. Physical Concept of Sound 10 1.2. Digital Representation and Sampling 11 1.3. A-law and µ-law 12 1.4. Linear Predictive Coding 13 2. Psychoacoustics 15 2.1. Absolute Threshold of Hearing 15 2.2. Critical Bands 16 2.3. Simultaneous Masking 17 2.4. Temporal Masking 20 2.5. Perceptual Entropy 20 3. Inside a Perceptual Encoder 22 3.1. Time-to-Frequency Domain Mapper 22 3.2. Mode Switching 26 3.3. Stereo Representation 28 3.4. Psychoacoustic Model 30 3.5. Quantizer 31 3.6. Entropy Coder 32 4. Comparing Modern Codecs 33 4.1. MPEG-1 Layer 3 (MP3) 33 4.2. MPEG-4 Advanced Audio Coding (AAC) 34 4.3. Dolby Digital (AC-3) 35 4.4. Vorbis I 36 4.5. Windows Media Audio 9 (WMA9) 37 4.6. Summary 38 3 References 39 4 List of Figures Figure 1 Audibility Threshold ................................................................................................................ 16 Figure 2 Noise Masking Tone (8) ........................................................................................................... 18 Figure 3 Tone Masking Noise (8) ..........................................................................................................
    [Show full text]
  • Adobe Media Encoder
    UNIVERSITY OF HOUSTON | SCHOOL OF ART | GRAPHIC DESIGN Spring 2018 ADOBE MEDIA ENCODER WORKING WITH DIGITAL VIDEO Digital video formats are different than other types of digital files you are used to working with. They are stored in what are called container formats. These are files with a variety of file extensions, Extra Reading like .mov .mpg .mkv, and features in order to combine different types of data into a single file. Rodrigues, Ana. 2016. “H.264 vs H.265 — A Technical Comparison. When Will H.265 Dominate the Market?” Inside that file, audio and video data types are encoded—packed up and compressed—withcodecs , Medium. June 9, 2016. https://medium.com/advanced- like H.264 or MPEG-2 for video and MP3 or AAC for audio. There are many types of codecs, but H.264 is computer-vision/h-264-vs-h-265-a-technical- comparison-when-will-h-265-dominate-the-market- the current reigning standard—and the one we will be using—with HEVC (H.265) expected to replace 26659303171a. it in the coming years as 4k devices and 4k media streaming become more common. Digital video editing requires a lot of hard drive space—enough space to store your video multiple times! That’s for the source media, “scratch space” which is used to store on-the-fly previews, and finally enough room to output the final video. You can prepare for this by moving large files off your computer. It is not preferable to actively edit video from an external harddrive, due to their slower throughput, though you may find it workable.
    [Show full text]
  • Ts 103 190 V1.1.1 (2014-04)
    ETSI TS 103 190 V1.1.1 (2014-04) Technical Specification Digital Audio Compression (AC-4) Standard 2 ETSI TS 103 190 V1.1.1 (2014-04) Reference DTS/JTC-025 Keywords audio, broadcasting, codec, content, digital, distribution ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N° 348 623 562 00017 - NAF 742 C Association à but non lucratif enregistrée à la Sous-Préfecture de Grasse (06) N° 7803/88 Important notice The present document can be downloaded from: http://www.etsi.org The present document may be made available in electronic versions and/or in print. The content of any electronic and/or print versions of the present document shall not be modified without the prior written authorization of ETSI. In case of any existing or perceived difference in contents between such versions and/or in print, the only prevailing document is the print of the Portable Document Format (PDF) version kept on a specific network drive within ETSI Secretariat. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current status of this and other ETSI documents is available at http://portal.etsi.org/tb/status/status.asp If you find errors in the present document, please send your comment to one of the following services: http://portal.etsi.org/chaircor/ETSI_support.asp Copyright Notification No part may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying and microfilm except as authorized by written permission of ETSI.
    [Show full text]
  • Constant Bitrate Control for a Distributed Video Coding System
    CONSTANT BITRATE CONTROL FOR A DISTRIBUTED VIDEO CODING SYSTEM Mariusz Jakubowski1, João Ascenso2 and Grzegorz Pastuszak1 1Institute of Radioelectronics, Warsaw University of Technology, 15/19 Nowowiejska Str., Warsaw, Poland 2Instituto Superior de Engenharia de Lisboa – Instituto de Telecomunicaçőes R. Conselheiro Emídio Navarro, 1, Lisbon, Portugal Keywords: Wyner-Ziv coding, distributed video coding, rate control. Abstract: In some distributed video coding (DVC) systems, the total bitrate depends mainly on the key frames (Intra coded) quality and on the side information accuracy. In this paper, a rate control (RC) mechanism is proposed to achieve and maintain a certain target bitrate for the overall Intra and WZ bitstream, mainly by adjusting online the Intra frames quality through the quantization parameter (QP). In order to obtain a similar decoded quality of Intra and WZ frames, the relevant parameters: QP for the key frames and the quantization index (QIndex) for WZ frames are controlled jointly. The major novelty of this work is a statistical model that expresses the relationship between QIndex and WZ frames bitrate. The proposed rate control solution is integrated into the VISNET2 WZ codec and the experimental results demonstrate the efficiency of the proposed algorithm to reach and maintain the target bitrate. 1 INTRODUCTION Y is explored at the decoder with reference to the case where joint encoding is performed (i.e. X and Y Around 2002, a new video coding paradigm known are available at the encoder). This interesting result as distributed video coding (DVC) has emerged, opens the possibility to design a system where two inspired by two Information Theory results from the statistically dependent signals are compressed in a 70’s: the Slepian-Wolf theorem (Slepian and Wolf, distributed way (separate encoding, joint decoding) 1973) and the Wyner-Ziv theorem (Wyner and Ziv, while still achieving the coding efficiency of 1976).
    [Show full text]
  • An Analysis of Constant Bitrate and Constant PSNR Encoding For
    An Analysis of Constant Bitrate and Constant PSNR Video Encoding for Wireless Networks Tanir Ozcelebi∗, Fabio De Vito†,A.MuratTekalp∗, M. Reha Civanlar∗, M. Oguz Sunay∗ & J. Carlos De Martin‡ ∗Koc University, College of Engineering, 34450, Istanbul, Turkey Email: {tozcelebi,osunay,mtekalp,[email protected]} †Dipartimento di Automatica e Informatica / ‡IEIIT-CNR, Politecnico di Torino, 10129, Torino, Italy Email: {fabio.devito,juancarlos.demartin}@polito.it Abstract— In wireless networks, transmission of constant- jobs, and the existing correlations between these can not be quality, high bitrate video is a challenging task due to channel exploited for better system efficiency. On the other hand, in capacity and buffer limitations. Content adaptive rate control, is multi-user environments, both network efficiency and video used as a solution to this problem. Instead of transmitting all of the video content at low quality, the most important content can quality can be considerably improved by considering network be transmitted at high quality while still preserving an acceptable statistics in video encoding. quality for the remaining segments. Furthermore, the rate control Another key point that can be considered in encoder rate strategy inside the individual temporal segments plays a key control is the video content. Adverse channel variations in role for the network performance and viewing quality. Although low-capacity wireless networks make it extremely difficult to constant quality video encoding inside the temporal segments is preferable for the best viewing experience, it causes more deliver constantly high quality video. Content adaptive inter- network packet losses due to adverse bitrate fluctuations in the temporal-segment rate control techniques for single-user video video stream.
    [Show full text]