COMPARISON of the CODING EFFICIENCY of VIDEO CODING STANDARDS 1671 Coefficients (Also Known As Transform Coefficient Levels)
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ACM Multimedia 2007
ACM Multimedia 2007 Call for Papers (PDF) University of Augsburg Augsburg, Germany September 24 – 29, 2007 http://www.acmmm07.org ACM Multimedia 2007 invites you to participate in the premier annual multimedia conference, covering all aspects of multimedia computing: from underlying technologies to applications, theory to practice, and servers to networks to devices. A final version of the conference program is now available. Registration will be open from 7:30am to 7pm on Monday and 7:30am to 5pm on Tuesday, Wednesday and Thursday On Friday registration is only possible from 7:30am to 1pm. ACM Business Meeting is schedule for Wednesday 9/26/2007 during Lunch (Mensa). Ramesh Jain and Klara Nahrstedt will talk about the SIGMM status SIGMM web status Status of other conferences/journals (TOMCCAP/MMSJ/…) sponsored by SIGMM Miscellaneous Also Chairs of MM’08 will give their advertisement Call for proposals for MM’09 + contenders will present their pitch Announcement of Euro ACM SIGMM Miscellaneous Travel information: How to get to Augsburg How to get to the conference sites (Augsburg public transport) Conference site maps and directions (excerpt from the conference program) Images of Augsburg The technical program will consist of plenary sessions and talks with topics of interest in: (a) Multimedia content analysis, processing, and retrieval; (b) Multimedia networking, sensor networks, and systems support; (c) Multimedia tools, end-systems, and applications; and (d) Multimedia interfaces; Awards will be given to the best paper and the best student paper as well as to best demo, best art program paper, and the best-contributed open-source software. -
VOL. E100-C NO. 6 JUNE 2017 the Usage of This PDF File Must Comply
VOL. E100-C NO. 6 JUNE 2017 The usage of this PDF file must comply with the IEICE Provisions on Copyright. The author(s) can distribute this PDF file for research and educational (nonprofit) purposes only. Distribution by anyone other than the author(s) is prohibited. IEICE TRANS. ELECTRON., VOL.E100–C, NO.6 JUNE 2017 643 PAPER A High-Throughput and Compact Hardware Implementation for the Reconstruction Loop in HEVC Intra Encoding Yibo FAN†a), Member, Leilei HUANG†, Zheng XIE†, and Xiaoyang ZENG†, Nonmembers SUMMARY In the newly finalized video coding standard, namely high 4×4, 8×8, 16×16, 32×32 and 64×64 with 35 possible pre- efficiency video coding (HEVC), new notations like coding unit (CU), pre- diction modes in intra prediction. Although several fast diction unit (PU) and transformation unit (TU) are introduced to improve mode decision designs have been proposed, still a consid- the coding performance. As a result, the reconstruction loop in intra en- coding is heavily burdened to choose the best partitions or modes for them. erable amount of candidate PU modes, PU partitions or TU In order to solve the bottleneck problems in cycle and hardware cost, this partitions are needed to be traversed by the reconstruction paper proposed a high-throughput and compact implementation for such a loop. reconstruction loop. By “high-throughput”, it refers to that it has a fixed It can be inferred that the reconstruction loop in intra throughput of 32 pixel/cycle independent of the TU/PU size (except for 4×4 TUs). By “compact”, it refers to that it fully explores the reusability prediction has become a bottleneck in cycle and hardware between discrete cosine transform (DCT) and inverse discrete cosine trans- cost. -
Dynamic Resource Management of Network-On-Chip Platforms for Multi-Stream Video Processing
DYNAMIC RESOURCE MANAGEMENT OF NETWORK-ON-CHIP PLATFORMS FOR MULTI-STREAM VIDEO PROCESSING Hashan Roshantha Mendis Doctor of Engineering University of York Computer Science March 2017 2 Abstract This thesis considers resource management in the context of parallel multiple video stream de- coding, on multicore/many-core platforms. Such platforms have tens or hundreds of on-chip processing elements which are connected via a Network-on-Chip (NoC). Inefficient task allo- cation configurations can negatively affect the communication cost and resource contention in the platform, leading to predictability and performance issues. Efficient resource management for large-scale complex workloads is considered a challenging research problem; especially when applications such as video streaming and decoding have dynamic and unpredictable workload characteristics. For these type of applications, runtime heuristic-based task mapping techniques are required. As the application and platform size increase, decentralised resource management techniques are more desirable to overcome the reliability and performance bot- tlenecks in centralised management. In this work, several heuristic-based runtime resource management techniques, targeting real-time video decoding workloads are proposed. Firstly, two admission control approaches are proposed; one fully deterministic and highly predictable; the other is heuristic-based, which balances predictability and performance. Secondly, a pair of runtime task mapping schemes are presented, which make use of limited known application properties, communication cost and blocking-aware heuristics. Combined with the proposed deterministic admission con- troller, these techniques can provide strict timing guarantees for hard real-time streams whilst improving resource usage. The third contribution in this thesis is a distributed, bio-inspired, low-overhead, task re-allocation technique, which is used to further improve the timeliness and workload distribution of admitted soft real-time streams. -
Parameter Optimization in H.265 Rate-Distortion by Single Frame
https://doi.org/10.2352/ISSN.2470-1173.2019.11.IPAS-262 © 2019, Society for Imaging Science and Technology Parameter optimization in H.265 Rate-Distortion by single- frame semantic scene analysis Ahmed M. Hamza; University of Portsmouth Abdelrahman Abdelazim; Blackpool and the Fylde College Djamel Ait-Boudaoud; University of Portsmouth Abstract with Q being the quantization value for the source. The relation is The H.265/HEVC (High Efficiency Video Coding) codec and derived based on several assumptions about the source probability its 3D extensions have crucial rate-distortion mechanisms that distribution within the quantization intervals, and the nature of help determine coding efficiency. We have introduced in this the rate-distortion relations themselves (constantly differentiable work a new system of Lagrangian parameterization in RDO cost throughout, etc.). The value initially used for c in the literature functions, based on semantic cues in an image, starting with the was 0.85. This was modified and made adaptive in subsequent current HEVC formulation of the Lagrangian hyper-parameter standards including HEVC/H.265. heuristics. Two semantic scenery flag algorithms are presented Our work here investigates further adaptations to the rate- and tested within the Lagrangian formulation as weighted factors. distortion Lagrangian by semantic algorithms, made possible by The investigation of whether the semantic gap between the coder recent frameworks in computer vision. and the image content is holding back the block-coding mecha- nisms as a whole from achieving greater efficiency has yielded a Adaptive Lambda Hyper-parameters in HEVC positive answer. Early versions of the rate-distortion parameters in H.263 were replaced with more sophisticated models in subsequent stan- Introduction dards. -
LOW COMPLEXITY H.264 to VC-1 TRANSCODER by VIDHYA
LOW COMPLEXITY H.264 TO VC-1 TRANSCODER by VIDHYA VIJAYAKUMAR Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE IN ELECTRICAL ENGINEERING THE UNIVERSITY OF TEXAS AT ARLINGTON AUGUST 2010 Copyright © by Vidhya Vijayakumar 2010 All Rights Reserved ACKNOWLEDGEMENTS As true as it would be with any research effort, this endeavor would not have been possible without the guidance and support of a number of people whom I stand to thank at this juncture. First and foremost, I express my sincere gratitude to my advisor and mentor, Dr. K.R. Rao, who has been the backbone of this whole exercise. I am greatly indebted for all the things that I have learnt from him, academically and otherwise. I thank Dr. Ishfaq Ahmad for being my co-advisor and mentor and for his invaluable guidance and support. I was fortunate to work with Dr. Ahmad as his research assistant on the latest trends in video compression and it has been an invaluable experience. I thank my mentor, Mr. Vishy Swaminathan, and my team members at Adobe Systems for giving me an opportunity to work in the industry and guide me during my internship. I would like to thank the other members of my advisory committee Dr. W. Alan Davis and Dr. William E Dillon for reviewing the thesis document and offering insightful comments. I express my gratitude Dr. Jonathan Bredow and the Electrical Engineering department for purchasing the software required for this thesis and giving me the chance to work on cutting edge technologies. -
Design and Implementation of a Fast HEVC Random Access Video Encoder
Design and Implementation of a Fast HEVC Random Access Video Encoder ALFREDO SCACCIALEPRE Master's Degree Project Stockholm, Sweden March 2014 XR-EE-KT 2014:003 Contents 1 Introduction 11 1.1 Background . 11 1.2 Thesis work . 12 1.2.1 Factors to consider . 12 1.3 The problem . 12 1.3.1 C65 . 12 1.4 Methods and thesis outline . 13 1.4.1 Methods . 13 1.4.2 Objective measurement . 14 1.4.3 Subjective measurement . 14 1.4.4 Test sequences . 14 1.4.5 Thesis outline . 16 1.4.6 Abbreviations . 16 2 General concepts 19 2.1 Color spaces . 19 2.2 Frames, slices and tiles . 19 2.2.1 Frames . 19 2.2.2 Slices and Tiles . 19 2.3 Predictions . 20 2.3.1 Intra . 20 2.3.2 Inter . 20 2.4 Merge mode . 20 2.4.1 Skip mode . 20 2.5 AMVP mode . 20 2.5.1 I, P and B frames . 21 2.6 CTU, CU, CTB, CB, PB, and TB . 21 2.7 Transforms . 23 2.8 Quantization . 24 2.9 Coding . 24 2.10 Reference picture lists . 24 2.11 Gop structure . 24 2.12 Temporal scalability . 25 2.13 Hierarchical B pictures . 25 2.14 Decoded picture buffer (DPB) . 25 2.15 Low delay and random access configurations . 26 2.16 H.264 and its encoders . 26 2.16.1 H.264 . 26 1 2 CONTENTS 3 Preliminary tests 27 3.1 Speed - quality considerations . 27 3.1.1 Interactive applications . 27 3.1.2 Entertainment applications . -
Fast Coding Unit Encoding Mechanism for Low Complexity Video Coding
RESEARCH ARTICLE Fast Coding Unit Encoding Mechanism for Low Complexity Video Coding Yuan Gao1,2,3☯, Pengyu Liu1,2,3☯, Yueying Wu1,2,3, Kebin Jia1,2,3*, Guandong Gao1,2,3 1 Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China, 2 Beijing Laboratory of Advanced Information Networks, Beijing, China, 3 College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China ☯ These authors contributed equally to this work. * [email protected] Abstract In high efficiency video coding (HEVC), coding tree contributes to excellent compression performance. However, coding tree brings extremely high computational complexity. Inno- vative works for improving coding tree to further reduce encoding time are stated in this OPEN ACCESS paper. A novel low complexity coding tree mechanism is proposed for HEVC fast coding Citation: Gao Y, Liu P, Wu Y, Jia K, Gao G (2016) unit (CU) encoding. Firstly, this paper makes an in-depth study of the relationship among Fast Coding Unit Encoding Mechanism for Low CU distribution, quantization parameter (QP) and content change (CC). Secondly, a CU Complexity Video Coding. PLoS ONE 11(3): coding tree probability model is proposed for modeling and predicting CU distribution. Even- e0151689. doi:10.1371/journal.pone.0151689 tually, a CU coding tree probability update is proposed, aiming to address probabilistic Editor: You Yang, Huazhong University of Science model distortion problems caused by CC. Experimental results show that the proposed low and Technology, CHINA complexity CU coding tree mechanism significantly reduces encoding time by 27% for lossy Received: September 23, 2015 coding and 42% for visually lossless coding and lossless coding. -
Extended Multiple Feature-Based Classifications for Adaptive Loop Filtering
SIP (2019),vol.8,e28,page1of11©TheAuthors,2019. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. doi:10.1017/ATSIP.2019.19 original paper Extended multiple feature-based classifications for adaptive loop filtering johannes erfurt,1‡ wang-q lim,1‡ heiko schwarz,1 detlev marpe1 andthomas wiegand 1,2 Recent progress in video compression is seemingly reaching its limits making it very hard to improve coding efficiency significantly further. The adaptive loop filter (ALF) has been a topic of interest for many years. ALF reaches high coding gains and has motivated many researchers over the past years to further improve the state-of-the-art algo- rithms. The main idea of ALF is to apply a classification to partition the set of all sample locations into multiple classes. After that, Wiener filters are calculated and applied for each class. Therefore, the performance of ALF essen- tially relies on how its classification behaves. In this paper, we extensively analyze multiple feature-based classifica- tions for ALF (MCALF) and extend the original MCALF by incorporating sample adaptive offset filtering. Further- more, we derive new block-based classifications which can be applied in MCALF to reduce its complexity. Experimental results show that our extended MCALF can further improve compression efficiency compared to the original MCALF algorithm. Keywords: Video compression, Adaptive loop filtering, Multiple classifications, SAO filtering Received 24 May 2019; Revised 06 October 2019 1. -
ITU-T Workshop Video and Image Coding and Applications (VICA)
ITU-T Workshop Video and Image Coding and Applications (VICA) BIO Geneva, 22-23 July 2005 Thomas Wiegand Assoc. Rapporteur ITU-T Q.6/16; HHI/ Germany Session: 2: Applications Title of Presentation: H.264/MPEG-4 AVC Application & Deployment Status Session: 6: Future trends in VICA Title of Presentation: New techniques for improved video coding Thomas Wiegand is the head of the Image Communication Group in the Image Processing Department of the Fraunhofer Institute for Telecommunications - Heinrich Hertz Institute Berlin, Germany. He received the Dipl.-Ing. degree in Electrical Engineering from the Technical University of Hamburg-Harburg, Germany, in 1995 and the Dr.-Ing. degree from the University of Erlangen-Nuremberg, Germany, in 2000. From 1993 to 1994, he was a Visiting Researcher at Kobe University, Japan. In 1995, he was a Visiting Scholar at the University of California at Santa Barbara, USA, where he started his research on video compression and transmission. Since then he has published a number of conference and journal papers on the subject and has contributed substantially to the ITU-T Video Coding Experts Group (ITU-T SG16 Q.6 - VCEG), ISO/IEC Moving Pictures Experts Group (ISO/IEC JTC1/SC29/WG11 - MPEG), and Joint Video Team (JVT) standardization efforts, and he holds various international patents in this field. From 1997 to 1998, was a Visiting Researcher at Stanford University, USA. In October 2000, he was appointed as the Associated Rapporteur of ITU-T VCEG. In December 2001, he was appointed as the Associated Rapporteur / Vice-Chair of the JVT. In February 2002, he was appointed as the Editor of the H.264/AVC video coding standard. -
Video Coding Standards
Module 8 Video Coding Standards Version 2 ECE IIT, Kharagpur Lesson 23 MPEG-1 standards Version 2 ECE IIT, Kharagpur Lesson objectives At the end of this lesson, the students should be able to : 1. Enlist the major video coding standards 2. State the basic objectives of MPEG-1 standard. 3. Enlist the set of constrained parameters in MPEG-1 4. Define the I- P- and B-pictures 5. Present the hierarchical data structure of MPEG-1 6. Define the macroblock modes supported by MPEG-1 23.0 Introduction In lesson 21 and lesson 22, we studied how to perform motion estimation and thereby temporally predict the video frames to exploit significant temporal redundancies present in the video sequence. The error in temporal prediction is encoded by standard transform domain techniques like the DCT, followed by quantization and entropy coding to exploit the spatial and statistical redundancies and achieve significant video compression. The video codecs therefore follow a hybrid coding structure in which DPCM is adopted in temporal domain and DCT or other transform domain techniques in spatial domain. Efforts to standardize video data exchange via storage media or via communication networks are actively in progress since early 1980s. A number of international video and audio standardization activities started within the International Telephone Consultative Committee (CCITT), followed by the International Radio Consultative Committee (CCIR), and the International Standards Organization / International Electrotechnical Commission (ISO/IEC). An experts group, known as the Motion Pictures Expects Group (MPEG) was established in 1988 in the framework of the Joint ISO/IEC Technical Committee with an objective to develop standards for coded representation of moving pictures, associated audio, and their combination for storage and retrieval of digital media. -
The H.264/MPEG4 Advanced Video Coding Standard and Its Applications
SULLIVAN LAYOUT 7/19/06 10:38 AM Page 134 STANDARDS REPORT The H.264/MPEG4 Advanced Video Coding Standard and its Applications Detlev Marpe and Thomas Wiegand, Heinrich Hertz Institute (HHI), Gary J. Sullivan, Microsoft Corporation ABSTRACT Regarding these challenges, H.264/MPEG4 Advanced Video Coding (AVC) [4], as the latest H.264/MPEG4-AVC is the latest video cod- entry of international video coding standards, ing standard of the ITU-T Video Coding Experts has demonstrated significantly improved coding Group (VCEG) and the ISO/IEC Moving Pic- efficiency, substantially enhanced error robust- ture Experts Group (MPEG). H.264/MPEG4- ness, and increased flexibility and scope of appli- AVC has recently become the most widely cability relative to its predecessors [5]. A recently accepted video coding standard since the deploy- added amendment to H.264/MPEG4-AVC, the ment of MPEG2 at the dawn of digital televi- so-called fidelity range extensions (FRExt) [6], sion, and it may soon overtake MPEG2 in further broaden the application domain of the common use. It covers all common video appli- new standard toward areas like professional con- cations ranging from mobile services and video- tribution, distribution, or studio/post production. conferencing to IPTV, HDTV, and HD video Another set of extensions for scalable video cod- storage. This article discusses the technology ing (SVC) is currently being designed [7, 8], aim- behind the new H.264/MPEG4-AVC standard, ing at a functionality that allows the focusing on the main distinct features of its core reconstruction of video signals with lower spatio- coding technology and its first set of extensions, temporal resolution or lower quality from parts known as the fidelity range extensions (FRExt). -
AVC to the Max: How to Configure Encoder
Contents Company overview …. ………………………………………………………………… 3 Introduction…………………………………………………………………………… 4 What is AVC….………………………………………………………………………… 6 Making sense of profiles, levels, and bitrate………………………………………... 7 Group of pictures and its structure..………………………………………………… 11 Macroblocks: partitioning and prediction modes….………………………………. 14 Eliminating spatial redundancy……………………………………………………… 15 Eliminating temporal redundancy……...……………………………………………. 17 Adaptive quantization……...………………………………………………………… 24 Deblocking filtering….….…………………………………………………………….. 26 Entropy encoding…………………………………….……………………………….. 2 8 Conclusion…………………………………………………………………………….. 29 Contact details..………………………………………………………………………. 30 2 www.elecard.com Company overview Elecard company, founded in 1988, is a leading provider of software products for encoding, decoding, processing, monitoring and analysis of video and audio data in 9700 companies various formats. Elecard is a vendor of professional software products and software development kits (SDKs); products for in - depth high - quality analysis and monitoring of the media content; countries 1 50 solutions for IPTV and OTT projects, digital TV broadcasting and video streaming; transcoding servers. Elecard is based in the United States, Russia, and China with 20M users headquarters located in Tomsk, Russia. Elecard products are highly appreciated and widely used by the leaders of IT industry such as Intel, Cisco, Netflix, Huawei, Blackmagic Design, etc. For more information, please visit www.elecard.com. 3 www.elecard.com Introduction Video compression is the key step in video processing. Compression allows broadcasters and premium TV providers to deliver their content to their audience. Many video compression standards currently exist in TV broadcasting. Each standard has different properties, some of which are better suited to traditional live TV while others are more suited to video on demand (VoD). Two basic standards can be identified in the history of video compression: • MPEG-2, a legacy codec used for SD video and early digital broadcasting.