Architectures and Codecs for Real-Time Light Field Streaming

Total Page:16

File Type:pdf, Size:1020Kb

Architectures and Codecs for Real-Time Light Field Streaming Reprinted from Journal of Imaging Science and Technology R 61(1): 010403-1–010403-13, 2017. c Society for Imaging Science and Technology 2017 Architectures and Codecs for Real-Time Light Field Streaming Péter Tamás Kovács Tampere University of Technology, Tampere, Finland Holografika, Budapest, Hungary E-mail: [email protected] Alireza Zare Tampere University of Technology, Tampere, Finland Nokia Technologies, Tampere, Finland Tibor Balogh Holografika, Budapest, Hungary Robert Bregovi¢ and Atanas Gotchev Tampere University of Technology, Tampere, Finland its unique set of associated technical challenges. Projection- Abstract. Light field 3D displays represent a major step forward in based light field 3D displays9–11 represent one of the visual realism, providing glasses-free spatial vision of real or virtual scenes. Applications that capture and process live imagery have to most practical and scalable approaches to glasses-free 3D process data captured by potentially tens to hundreds of cameras visualization, achieving, as of today, a 100 Mpixel total 3D and control tens to hundreds of projection engines making up the resolution and supporting continuous parallax effect. human perceivable 3D light field using a distributed processing Various applications where 3D spatial visualization has system. The associated massive data processing is difficult to scale beyond a specific number and resolution of images, limited by the added value have been proposed for 3D displays. Some capabilities of the individual computing nodes. The authors therefore of these applications involve displaying artificial/computer analyze the bottlenecks and data flow of the light field conversion generated content, such as CAD design, service and mainte- process and identify possibilities to introduce better scalability. Based on this analysis they propose two different architectures for nance training, animated movies and 3D games, driving and distributed light field processing. To avoid using uncompressed video flight simulation. Other applications require the capturing, data all along the processing chain, the authors also analyze how transmission/storage and rendering of 3D imagery, such the operation of the proposed architectures can be supported by as 3D TV and 3D video conferencing (3D telepresence). existing image/video codecs. c 2017 Society for Imaging Science and Technology. From the second group, applications that require live 3D image transmission are the technically most demanding, as 3D visual information has much more information content compared to its 2D counterpart. In state of the INTRODUCTION art 3D displays, total pixel count can be 1–2 orders of Three-dimensional (3D) displays1,2 represent a new class magnitude higher than in common 2D displays, making such of terminal devices, that make a major step forward in applications extremely data intensive. realism toward displays that can show imagery indistin- We focus on the problems associated with applications guishable from reality. 3D display technologies use different that require real-time streaming of live 3D video. Weconsider approaches to make the human eyes see spatial information. projection-based light field 3D displays as the underlying While the most straightforward approaches use glasses or display technology (with tens or hundreds of projection other headgear to achieve separation of left and right views, engines) coupled with massive multi-camera arrays for autostereoscopic displays achieve similar effects without the light field capturing (with tens or hundreds of cameras). necessity of wearing special glasses. Typical autostereoscopic We explore possibilities that can potentially work in real display technologies include parallax barrier,3 lenticular time on today's hardware. Please note that the majority lens,4 volumetric,5 light field6,7 and pure holographic8 of the problems discussed here also apply to other 3D displays, most of them available commercially, each with capture and display setups in which the necessary data throughput cannot be handled by a single data processing node, and as such distributing the workload becomes Received July 17, 2016; accepted for publication Oct. 30, 2016; published online Dec. 20, 2016. Associate Editor: Hideki Kakeya. necessary. Our novel contribution presented in this article lies in: analysis of the typical data flow taking place during IS&T International Symposium on Electronic Imaging 2017 54 Stereoscopic Displays and Applications XXVIII converting multiview content to display-specific light field representation; analysis of bottlenecks in a direct (brute force) light field conversion process; presentation of two possible approaches for eliminating such bottlenecks; and a suitability analysis of existing image and video codecs for supporting the proposed approaches. The article is organized as follows. First light fields and light field displays, as well as different content generation and rendering methods for light field displays are introduced. Then the proposed architectures for scalable light field decompression and light field conversion are described, followed by a comparative analysis of the discussed architec- tures and codecs. Figure 1. Basic principle of projection-based light field displays: projection modules project light rays from the back, toward the LIGHT FIELDS, LIGHT FIELD DISPLAYS AND holographic screen. Object points are visible where two light rays appear CONTENT CREATION to cross each other consistently. Human eyes can see different imagery at The propagation of visible light rays in space can be described different locations due to the direction selective light emission property of the screen. by the plenoptic function, which is a 7D continuous function, parameterized by location in 3D space (3 parameters), angles 180◦, the image can be walked around. The 3D image can be of observation (2 parameters), wavelength and time.12 In observed without glasses, and displayed objects can appear real-world implementations, a light field, which is a simpli- floating in front of the screen, or behind the screen surface, fied 3D or 4D parameterization of the plenoptic function, is like with holograms. used, which allows to represent visual information in terms of This image formation is achieved by controlling the rays with their positions in space and directionality.13,14 In a light rays so that they mimic the light field that would be 4D light field, ray positions are identified by either using two present if the real object would be placed to the same physical planes and the hit point of the light ray on both planes, or by space, up to the angular resolution provided by the projection using a single hit point on a plane and the direction to which modules. Such glasses-free display technology has dramatic the light ray propagates. This 4D light field describes a static advantages in application fields like 3D TV,and especially 3D light field in a half-space. For a more detailed description, the video conferencing, where true eye contact between multiple reader is referred to Ref. 15. participants at both sides can only be achieved by using Having a light field properly reproduced will provide a 3D display.16 These same applications require real-time the user with 3D effects such a binocularity and continuous processing (capture, transmission, and visualization) of live motion parallax. Today's light field displays can typically images, which has special requirements on the rendering reproduce a horizontal-only-parallax light field, which allows system. the simplification of the light field representation to 3D. Projection-based light field displays from Holografika have been used for the purposes of this work. The interested Optical System and Image Properties reader is referred to Ref.7 for a complete description of the Projection-based light field displays are based on a massive display system. Note that other projection-based light field array of projection modules that project light rays onto a displays based on very similar architectures also exist;10,11,17 special holographic screen to reproduce an approximation thus our findings are relevant for those displays too. of the continuous light field. The light rays originating from one source (later referred to as light field slices) hit the Rendering System and Data Flow screen at different positions all over the screen surface and The previously described distributed optical system is served pass through the screen while maintaining their directions. by a parallel rendering system, implemented as a cluster of Light rays from other sources will also hit the screen all over multi-GPU computers. In the simple case, each GPU output the screen surface, but from slightly different directions (see drives a single optical module, multiple outputs of the same Figure1 ). The light rays that cross the screen at the same GPU correspond to adjacent optical modules, while one screen position but in different directions make up a pixel computer (having multiple GPUs) corresponds to a bigger that has the direction selective light emission property, which group of adjacent modules. As in Ref. 18, we define the part is a necessary condition to create glasses-free 3D displays. of the whole light field that is reproduced by a single optical By showing different but consistent information to slightly module a light field slice. One such slice is not visible from a different directions, it is possible to show different images to single point of observation, nor does it represent a single
Recommended publications
  • Panstamps Documentation Release V0.5.3
    panstamps Documentation Release v0.5.3 Dave Young 2020 Getting Started 1 Installation 3 1.1 Troubleshooting on Mac OSX......................................3 1.2 Development...............................................3 1.2.1 Sublime Snippets........................................4 1.3 Issues...................................................4 2 Command-Line Usage 5 3 Documentation 7 4 Command-Line Tutorial 9 4.1 Command-Line..............................................9 4.1.1 JPEGS.............................................. 12 4.1.2 Temporal Constraints (Useful for Moving Objects)...................... 17 4.2 Importing to Your Own Python Script.................................. 18 5 Installation 19 5.1 Troubleshooting on Mac OSX...................................... 19 5.2 Development............................................... 19 5.2.1 Sublime Snippets........................................ 20 5.3 Issues................................................... 20 6 Command-Line Usage 21 7 Documentation 23 8 Command-Line Tutorial 25 8.1 Command-Line.............................................. 25 8.1.1 JPEGS.............................................. 28 8.1.2 Temporal Constraints (Useful for Moving Objects)...................... 33 8.2 Importing to Your Own Python Script.................................. 34 8.2.1 Subpackages.......................................... 35 8.2.1.1 panstamps.commonutils (subpackage)........................ 35 8.2.1.2 panstamps.image (subpackage)............................ 35 8.2.2 Classes............................................
    [Show full text]
  • Discrete Cosine Transform for 8X8 Blocks with CUDA
    Discrete Cosine Transform for 8x8 Blocks with CUDA Anton Obukhov [email protected] Alexander Kharlamov [email protected] October 2008 Document Change History Version Date Responsible Reason for Change 0.8 24.03.2008 Alexander Kharlamov Initial release 0.9 25.03.2008 Anton Obukhov Added algorithm-specific parts, fixed some issues 1.0 17.10.2008 Anton Obukhov Revised document structure October 2008 2 Abstract In this whitepaper the Discrete Cosine Transform (DCT) is discussed. The two-dimensional variation of the transform that operates on 8x8 blocks (DCT8x8) is widely used in image and video coding because it exhibits high signal decorrelation rates and can be easily implemented on the majority of contemporary computing architectures. The key feature of the DCT8x8 is that any pair of 8x8 blocks can be processed independently. This makes possible fully parallel implementation of DCT8x8 by definition. Most of CPU-based implementations of DCT8x8 are firmly adjusted for operating using fixed point arithmetic but still appear to be rather costly as soon as blocks are processed in the sequential order by the single ALU. Performing DCT8x8 computation on GPU using NVIDIA CUDA technology gives significant performance boost even compared to a modern CPU. The proposed approach is accompanied with the sample code “DCT8x8” in the NVIDIA CUDA SDK. October 2008 3 1. Introduction The Discrete Cosine Transform (DCT) is a Fourier-like transform, which was first proposed by Ahmed et al . (1974). While the Fourier Transform represents a signal as the mixture of sines and cosines, the Cosine Transform performs only the cosine-series expansion.
    [Show full text]
  • RISC-V Software Ecosystem
    RISC-V Software Ecosystem Palmer Dabbelt [email protected] UC Berkeley February 8, 2015 Software on RISC-V So it turns out there is a lot of software... 2 Software on RISC-V sys-libs/zlib-1.2.8-r1 media-libs/libjpeg-turbo-1.3.1-r1 virtual/shadow-0 virtual/libintl-0-r1 sys-apps/coreutils-8.23 sys-apps/less-471 sys-libs/ncurses-5.9-r3 sys-libs/readline-6.3 p8-r2 app-admin/eselect-python-20140125 sys-apps/gentoo-functions-0.8 sys-libs/glibc-2.20-r1 sys-apps/grep-2.21-r1 dev-libs/gmp-6.0.0a sys-apps/util-linux-2.25.2-r2 virtual/service-manager-0 sys-libs/db-6.0.30-r1 sys-apps/sed-4.2.2 virtual/editor-0 virtual/libiconv-0-r1 sys-apps/file-5.22 sys-devel/gcc-4.9.2-r1 app-arch/bzip2-1.0.6-r7 dev-libs/mpfr-3.1.2 p10 x11-libs/libX11-1.6.2 sys-apps/busybox-1.23.0-r1 sys-process/psmisc-22.21-r2 virtual/pager-0 sys-devel/gcc-config-1.8 net-misc/netifrc-0.3.1 x11-libs/libXext-1.3.3 sys-libs/timezone-data-2014j dev-libs/popt-1.16-r2 x11-libs/libXfixes-5.0.1 app-misc/editor-wrapper-4 sys-devel/binutils-config-4-r1 x11-libs/libXt-1.1.4 net-firewall/iptables-1.4.21-r1 virtual/libffi-3.0.13-r1 x11-libs/fltk-1.3.3-r2 sys-libs/e2fsprogs-libs-1.42.12 sys-libs/cracklib-2.9.2 x11-libs/libXi-1.7.4 dev-libs/libpipeline-1.4.0 sys-apps/kmod-19 x11-libs/libXtst-1.2.2 sys-libs/gdbm-1.11 sys-devel/make-4.1-r1 net-misc/tigervnc-1.3.1-r2 app-portage/portage-utils-0.53 sys-process/procps-3.3.10-r1 dev-lang/perl-5.20.1-r4 sys-apps/sandbox-2.6-r1 sys-apps/iproute2-3.18.0 app-admin/perl-cleaner-2.19 app-misc/pax-utils-0.9.2 virtual/dev-manager-0 perl-core/Data-Dumper-2.154.0
    [Show full text]
  • I Deb, You Deb, Everybody Debs: Debian Packaging For
    I deb, you deb, everybody debs Debian packaging for beginners and experts alike Ondřej Surý • [email protected][email protected] • 25.­ 10. 2017 Contents ● .deb binary package structure ● Source package structure ● Basic toolchain ● Recommended toolchain ● Keeping the sources in git ● Clean build environment ● Misc... ● So how do I become Debian Developer? My Debian portfolio (since 2000) ● Mostly team maintained ● BIRD ● PHP + PECL (pkg-php) ● Cyrus SASL ○ Co-installable packages since 7.x ● Cyrus IMAPD ○ Longest serving PHP maintainer in Debian ● Apache2 + mod_md (fresh) (and still not crazy) ● ...other little stuff ● libjpeg-turbo ○ Transitioned Debian from IIJ JPEG (that Older work crazy guy) to libjpeg-turbo ● DNS Packaging Group ● GTK/GNOME/Freedesktop ○ CZ.NIC’s Knot DNS and Knot Resolver ● Redmine/Ruby ○ NLnet Lab’s NSD, Unbound, getdns, ldns Never again, it’s a straight road to madness ○ PowerDNS ○ ○ BIND 9 ● Berkeley DB and LMDB (pkg-db) ○ One Berkeley DB per release (yay!) Binary package structure ● ar archive consisting of: $ ar xv knot_2.0.1-4_amd64.deb x – debian-binary ○ debian-binary x – control.tar.gz x – data.tar.xz ■ .deb format version (2.0) ○ control.tar.gz $ dpkg-deb -X knot_2.0.1-4_amd64.deb output/ ./ ■ Package informatio (control) ./etc/ ■ Maintainer scripts […] ./usr/sbin/knotd ● {pre,post}{inst,rm} […] Misc (md5sum, conffiles) ■ $ dpkg-deb -e knot_2.0.1-4_amd64.deb DEBIAN/ ○ data.tar.xz $ ls DEBIAN/ conffiles control md5sums postinst postrm preinst ■ Actual content of the package prerm ■ This is what gets installed $ dpkg -I knot_2.0.1-4_amd64.deb ● Nástroje pro práci s .deb soubory new debian package, version 2.0.
    [Show full text]
  • An Optimization of JPEG-LS Using an Efficient and Low-Complexity
    Received April 26th, 2021. Revised June 27th, 2021. Accepted July 23th, 2021. Digital Object Identifier 10.1109/ACCESS.2021.3100747 LOCO-ANS: An Optimization of JPEG-LS Using an Efficient and Low-Complexity Coder Based on ANS TOBÍAS ALONSO , GUSTAVO SUTTER , AND JORGE E. LÓPEZ DE VERGARA High Performance Computing and Networking Research Group, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Spain. {tobias.alonso, gustavo.sutter, jorge.lopez_vergara}@uam.es This work was supported in part by the Spanish Research Agency under the project AgileMon (AEI PID2019-104451RB-C21). ABSTRACT Near-lossless compression is a generalization of lossless compression, where the codec user is able to set the maximum absolute difference (the error tolerance) between the values of an original pixel and the decoded one. This enables higher compression ratios, while still allowing the control of the bounds of the quantization errors in the space domain. This feature makes them attractive for applications where a high degree of certainty is required. The JPEG-LS lossless and near-lossless image compression standard combines a good compression ratio with a low computational complexity, which makes it very suitable for scenarios with strong restrictions, common in embedded systems. However, our analysis shows great coding efficiency improvement potential, especially for lower entropy distributions, more common in near-lossless. In this work, we propose enhancements to the JPEG-LS standard, aimed at improving its coding efficiency at a low computational overhead, particularly for hardware implementations. The main contribution is a low complexity and efficient coder, based on Tabled Asymmetric Numeral Systems (tANS), well suited for a wide range of entropy sources and with simple hardware implementation.
    [Show full text]
  • LIBJPEG the Independent JPEG Group's JPEG Software README for Release 6B of 27-Mar-1998 This Distribution Contains the Sixth
    LIBJPEG The Independent JPEG Group's JPEG software README for release 6b of 27-Mar-1998 This distribution contains the sixth public release of the Independent JPEG Group's free JPEG software. You are welcome to redistribute this software and to use it for any purpose, subject to the conditions under LEGAL ISSUES, below. Serious users of this software (particularly those incorporating it into larger programs) should contact IJG at [email protected] to be added to our electronic mailing list. Mailing list members are notified of updates and have a chance to participate in technical discussions, etc. This software is the work of Tom Lane, Philip Gladstone, Jim Boucher, Lee Crocker, Julian Minguillon, Luis Ortiz, George Phillips, Davide Rossi, Guido Vollbeding, Ge' Weijers, and other members of the Independent JPEG Group. IJG is not affiliated with the official ISO JPEG standards committee. DOCUMENTATION ROADMAP This file contains the following sections: OVERVIEW General description of JPEG and the IJG software. LEGAL ISSUES Copyright, lack of warranty, terms of distribution. REFERENCES Where to learn more about JPEG. ARCHIVE LOCATIONS Where to find newer versions of this software. RELATED SOFTWARE Other stuff you should get. FILE FORMAT WARS Software *not* to get. TO DO Plans for future IJG releases. Other documentation files in the distribution are: User documentation: install.doc How to configure and install the IJG software. usage.doc Usage instructions for cjpeg, djpeg, jpegtran, rdjpgcom, and wrjpgcom. *.1 Unix-style man pages for programs (same info as usage.doc). wizard.doc Advanced usage instructions for JPEG wizards only.
    [Show full text]
  • Compiling Your Own Perl
    APPENDIX A Compiling Your Own Perl Compiling Perl on a Unix-like system is simple. First, obtain the source for Perl from CPAN (dppl6++_l]j*lanh*knc+on_+NA=@IA*dpih). Then input the following sequence of commands: p]nvtrblanh)1*4*4*p]n*cv _`lanh)1*4*4 od?kjbecqna)`ao i]ga i]gapaop oq`ki]gaejop]hh On most Unix systems, this code will result in your lanh being installed into the +qon+ hk_]h+ directory tree. If you want it installed elsewhere—for example, in the local directory in your home directory—then replace od?kjbecqna)`a with the following: od?kjbecqna)`ao)@lnabet9z+hk_]h+ which should enable you to install Perl on your computer without root access. Note that the )`ao flag uses all the default options for compiling Perl. If you know that you want nonstandard configuration, just use the flag )`a instead to be prompted for your requirements. Be aware that the source for Perl 5.10.0 requires a patch to work properly with Catalyst. This is fixed in subsequent versions of Perl 5.10. If you need to test code guaranteed to run on a wide range of systems, you should con- sider using Perl version 5.8.7. Perl versions greater than 5.8.7 contain features that were not available in earlier versions of Perl, so Perl 5.8.7 is feature complete for all versions of Perl that Catalyst will run on (version 5.8.1 and later). Put another way, versions 5.8.8 and later have new features that you can’t rely on in earlier releases.
    [Show full text]
  • Daala: a Perceptually-Driven Still Picture Codec
    DAALA: A PERCEPTUALLY-DRIVEN STILL PICTURE CODEC Jean-Marc Valin, Nathan E. Egge, Thomas Daede, Timothy B. Terriberry, Christopher Montgomery Mozilla, Mountain View, CA, USA Xiph.Org Foundation ABSTRACT and vertical directions [1]. Also, DC coefficients are com- bined recursively using a Haar transform, up to the level of Daala is a new royalty-free video codec based on perceptually- 64x64 superblocks. driven coding techniques. We explore using its keyframe format for still picture coding and show how it has improved over the past year. We believe the technology used in Daala Multi-Symbol Entropy Coder could be the basis of an excellent, royalty-free image format. Most recent video codecs encode information using binary arithmetic coding, meaning that each symbol can only take 1. INTRODUCTION two values. The Daala range coder supports up to 16 values per symbol, making it possible to encode fewer symbols [6]. Daala is a royalty-free video codec designed to avoid tra- This is equivalent to coding up to four binary values in parallel ditional patent-encumbered techniques used in most cur- and reduces serial dependencies. rent video codecs. In this paper, we propose to use Daala’s keyframe format for still picture coding. In June 2015, Daala was compared to other still picture codecs at the 2015 Picture Perceptual Vector Quantization Coding Symposium (PCS) [1,2]. Since then many improve- Rather than use scalar quantization like the vast majority of ments were made to the bitstream to improve its quality. picture and video codecs, Daala is based on perceptual vector These include reduced overlap in the lapped transform, finer quantization (PVQ) [7].
    [Show full text]
  • Faster Neural Networks Straight from Jpeg
    Workshop track - ICLR 2018 FASTER NEURAL NETWORKS STRAIGHT FROM JPEG Lionel Gueguen & Alex Sergeev Rosanne Liu & Jason Yosinski Uber Technologies Uber AI Labs San Francisco, CA 94103, USA San Francisco, CA 94103, USA flgueguen, [email protected] frosanne, [email protected] ABSTRACT Training CNNs directly from RGB pixels has enjoyed overwhelming empirical success. But can more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. We modify libjpeg to produce DCT coefficients directly, modify a ResNet-50 network to accommodate the differently sized and strided input, and evaluate performance on ImageNet. We find networks that are both faster and more accurate, as well as networks with about the same accuracy but 1.77x faster than ResNet-50. 1 INTRODUCTION Progresses toward training convolutional neural networks on a variety of tasks (Krizhevsky et al., 2012; Mnih et al., 2013; Ren et al., 2015; He et al., 2015) has led to the widespread adoption of such models in both academia and industry. Traditionally CNNs are trained with input provided as an array of red-green-blue (RGB) pixels. In this paper we propose and explore a simple idea for accelerating neural network training and inference where networks are applied to images encoded in the JPEG format. We modify the libjpeg library to decode JPEG images only partially, resulting in an image representation consisting of a triple of tensors containing discrete cosine transform (DCT) coefficients in the YCbCr color space.
    [Show full text]
  • Faster Neural Networks Straight from JPEG
    Faster Neural Networks Straight from JPEG Lionel Gueguen1 Alex Sergeev1 Ben Kadlec1 Rosanne Liu2 Jason Yosinski2 1Uber 2Uber AI Labs flgueguen,asergeev,bkadlec,rosanne,[email protected] Abstract The simple, elegant approach of training convolutional neural networks (CNNs) directly from RGB pixels has enjoyed overwhelming empirical success. But could more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. Intuitively, when processing JPEG images using CNNs, it seems unnecessary to decompress a blockwise frequency representation to an expanded pixel representation, shuffle it from CPU to GPU, and then process it with a CNN that will learn something similar to a transform back to frequency representation in its first layers. Why not skip both steps and feed the frequency domain into the network directly? In this paper, we modify libjpeg to produce DCT coefficients directly, modify a ResNet-50 network to accommodate the differently sized and strided input, and evaluate performance on ImageNet. We find networks that are both faster and more accurate, as well as networks with about the same accuracy but 1.77x faster than ResNet-50. 1 Introduction The amazing progress toward training neural networks, particularly convolutional neural networks [14], to attain good performance on a variety of tasks [13, 19, 20, 10] has led to the widespread adoption of such models in both academia and industry. When CNNs are trained using image data as input, data is most often provided as an array of red-green-blue (RGB) pixels.
    [Show full text]
  • Red Hat Enterprise Linux 7 7.8 Release Notes
    Red Hat Enterprise Linux 7 7.8 Release Notes Release Notes for Red Hat Enterprise Linux 7.8 Last Updated: 2021-03-02 Red Hat Enterprise Linux 7 7.8 Release Notes Release Notes for Red Hat Enterprise Linux 7.8 Legal Notice Copyright © 2021 Red Hat, Inc. The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/ . In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, the Red Hat logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent. Red Hat is not formally related to or endorsed by the official Joyent Node.js open source or commercial project.
    [Show full text]
  • Database-Installation-Guide-Linux.Pdf
    Oracle® Database Database Installation Guide 19c for Linux E96432-19 July 2021 Oracle Database Database Installation Guide, 19c for Linux E96432-19 Copyright © 2015, 2021, Oracle and/or its affiliates. Primary Authors: Prakash Jashnani, Subhash Chandra Contributing Authors: Douglas Williams Contributors: Jean-Francois Verrier, Richard Roddy, Neha Avasthy, Sampath Ravindhran, Prasad Bagal, Subhranshu Banerjee, Gerald Venzl, Tammy Bednar, Avi Miller, Gavin Bowe, Gia-Khanh Nguyen, Darcy Christensen, Kiran Chamala, Jonathan Creighton, Benoit Dageville, Logeshwaran Rajan, Rajesh Dasari, Angad Gokakkar , Anu Natarajan, Girdhari Ghantiyala, Bernard Clouse, Chandrasekharan Iyer, Anil Nair, Sivaram Soma, Lisa Vaz, Ranjit Noronha, Vasu Venkatasubramanian, Sumanta Chatterjee, Margaret Susairaj, Malai Stalin, Markus Michalewicz, Subrahmanyam Kodavaluru, Sudip Datta, Madhu Hunasigi, Jim Erickson, Marcus Fallen, Joseph Francis, Allan Graves, Barbara Glover, Asad Hasan, Thirumaleshwara Hasandka, Putta Ramesh, Sergio Leunissen, Aneesh Khandelwal, Joel Kallman, Eugene Karichkin, Jai Krishnani, Prasad K Kulkarni, Ranjith Kundapur, Balaji Pagadala, Christopher Jones, Tak Wang, Bryn Llewellyn, Saar Maoz, Chao Liang, Gopal Mulagund, Pushkar Punit, Sivaselvam Narayanasamy, Ankur Kemkar, Sue Lee, Rich Long, Ricardo Alonso Gonzalez Acuna, Barb Lundhild, Sangeeth Jose, Rudregowda Mallegowda, Prasad Kuruvadi Nagaraj, Mughees Minhas, Krishna Mohan, Matthew McKerley, John McHugh, Gurudas Pai, Satish Panchumarthy , Rajesh Prasad, Rajendra Pingte, Ramesh Chakravarthula,
    [Show full text]