High Performance Video Encoding with Nvidia Gpus
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												  An Effective Self-Adaptive Policy for Optimal Video Quality Over Heterogeneous Mobile Devices and Network Discovery ServicesAppl. Math. Inf. Sci. 13, No. 3, 489-505 (2019) 489 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.18576/amis/130322 An Effective Self-Adaptive Policy for Optimal Video Quality over Heterogeneous Mobile Devices and Network Discovery Services Saleh Ali Alomari∗1 , Mowafaq Salem Alzboon1, Belal Zaqaibeh1 and Mohammad Subhi Al-Batah1 1Faculty of Science and Information Technology, Jadara University, 21110 Irbid, Jordan Received: 12 Dec. 2018, Revised: 1 Feb. 2019, Accepted: 20 Feb. 2019 Published online: 1 May 2019 Abstract: The Video on Demand (VOD) system is considered a communicating multimedia system that can allow clients be interested whilst watching a video of their selection anywhere and anytime upon their convenient. The design of the VOD system is based on the process and location of its three basic contents, which are: the server, network configuration and clients. The clients are varied from numerous approaches, battery capacities, involving screen resolutions, capabilities and decoder features (frame rates, spatial dimensions and coding standards). The up-to-date systems deliver VOD services through to several devices by utilising the content of a single coded video without taking into account various features and platforms of a device, such as WMV9, 3GPP2 codec, H.264, FLV, MPEG-1 and XVID. This limitation only provides existing services to particular devices that are only able to play with a few certain videos. Multiple video codecs are stored by VOD systems store for a similar video into the storage server. The problems caused by the bandwidth overhead arise once the layers of a video are produced.
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												  Download Media Player Codec Pack Version 4.1 Media Player Codec Packdownload media player codec pack version 4.1 Media Player Codec Pack. Description: In Microsoft Windows 10 it is not possible to set all file associations using an installer. Microsoft chose to block changes of file associations with the introduction of their Zune players. Third party codecs are also blocked in some instances, preventing some files from playing in the Zune players. A simple workaround for this problem is to switch playback of video and music files to Windows Media Player manually. In start menu click on the "Settings". In the "Windows Settings" window click on "System". On the "System" pane click on "Default apps". On the "Choose default applications" pane click on "Films & TV" under "Video Player". On the "Choose an application" pop up menu click on "Windows Media Player" to set Windows Media Player as the default player for video files. Footnote: The same method can be used to apply file associations for music, by simply clicking on "Groove Music" under "Media Player" instead of changing Video Player in step 4. Media Player Codec Pack Plus. Codec's Explained: A codec is a piece of software on either a device or computer capable of encoding and/or decoding video and/or audio data from files, streams and broadcasts. The word Codec is a portmanteau of ' co mpressor- dec ompressor' Compression types that you will be able to play include: x264 | x265 | h.265 | HEVC | 10bit x265 | 10bit x264 | AVCHD | AVC DivX | XviD | MP4 | MPEG4 | MPEG2 and many more. File types you will be able to play include: .bdmv | .evo | .hevc | .mkv | .avi | .flv | .webm | .mp4 | .m4v | .m4a | .ts | .ogm .ac3 | .dts | .alac | .flac | .ape | .aac | .ogg | .ofr | .mpc | .3gp and many more.
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												  Encoding H.264 Video for Streaming and Progressive DownloadWhat is Tuning? • Disable features that: • Improve subjective video quality but • Degrade objective scores • Example: adaptive quantization – changes bit allocation over frame depending upon complexity • Improves visual quality • Looks like “error” to metrics like PSNR/VMAF What is Tuning? • Switches in encoding string that enables tuning (and disables these features) ffmpeg –input.mp4 –c:v libx264 –tune psnr output.mp4 • With x264, this disables adaptive quantization and psychovisual optimizations Why So Important • Major point of contention: • “If you’re running a test with x264 or x265, and you wish to publish PSNR or SSIM scores, you MUST use –tune PSNR or –tune SSIM, or your results will be completely invalid.” • http://x265.org/compare-video-encoders/ • Absolutely critical when comparing codecs because some may or may not enable these adjustments • You don’t have to tune in your tests; but you should address the issue and explain why you either did or didn’t Does Impact Scores • 3 mbps football (high motion, lots of detail) • PSNR • No tuning – 32.00 dB • Tuning – 32.58 dB • .58 dB • VMAF • No tuning – 71.79 • Tuning – 75.01 • Difference – over 3 VMAF points • 6 is JND, so not a huge deal • But if inconsistent between test parameters, could incorrectly show one codec (or encoding configuration) as better than the other VQMT VMAF Graph Red – tuned Green – not tuned Multiple frames with 3-4-point differentials Downward spikes represent untuned frames that metric perceives as having lower quality Tuned Not tuned Observations • Tuning
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												  Screen Capture Tools to Record Online Tutorials This Document Is Made to Explain How to Use Ffmpeg and Quicktime to Record Mini Tutorials on Your Own ComputerScreen capture tools to record online tutorials This document is made to explain how to use ffmpeg and QuickTime to record mini tutorials on your own computer. FFmpeg is a cross-platform tool available for Windows, Linux and Mac. Installation and use process depends on your operating system. This info is taken from (Bellard 2016). Quicktime Player is natively installed on most of Mac computers. This tutorial focuses on Linux and Mac. Table of content 1. Introduction.......................................................................................................................................1 2. Linux.................................................................................................................................................1 2.1. FFmpeg......................................................................................................................................1 2.1.1. installation for Linux..........................................................................................................1 2.1.1.1. Add necessary components........................................................................................1 2.1.2. Screen recording with FFmpeg..........................................................................................2 2.1.2.1. List devices to know which one to record..................................................................2 2.1.2.2. Record screen and audio from your computer...........................................................3 2.2. Kazam........................................................................................................................................4
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												  GPU-Based Deep Learning InferenceWhitepaper GPU-Based Deep Learning Inference: A Performance and Power Analysis November 2015 1 Contents Abstract ......................................................................................................................................................... 3 Introduction .................................................................................................................................................. 3 Inference versus Training .............................................................................................................................. 4 GPUs Excel at Neural Network Inference ..................................................................................................... 5 Inference Optimizations in Caffe and cuDNN 4 ........................................................................................ 5 Experimental Setup and Testing Methodology ........................................................................................ 7 Inference on Small and Large GPUs .......................................................................................................... 8 Conclusion ................................................................................................................................................... 10 References .................................................................................................................................................. 10 2 Abstract Deep learning methods are revolutionizing various areas of machine perception. On a
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												  Spin Digital HEVC/H.265 Software Encoder (Spin Enc) Enables Ultra-High-Quality Video with the Highest Compression LevelSpin Digital HEVC/H.265 software encoder (Spin Enc) enables ultra-high-quality video with the highest compression level. Encoding, transmission, and storage of video in 4K, 8K, and beyond are now possible with commodity computing technologies. HEVC/H.265 encoder for production, contribution, and distribution of professional video for broadcast, VoD, and creative studios. Spin Digital HEVC/H.265 encoder is ready for the next generation of high-quality video systems, providing support for Ultra HD (UHD), High Dynamic Range (HDR), High Frame Rate (HFR), Wide Color Gamut (WCG), and Virtual Reality (360° video). Product Highlights HEVC/H.265 Encoder Package • Fast offline encoding software solution • Encoder: standalone HEVC/H.265 encoder • Ready for 8K and beyond • Transcoder: HEVC/H.265 decoder and encoder • Significantly better compression and quality integrated in FFmpeg than competing encoders • SDK: encoder plugin for FFmpeg (Libavcodec) • Enables WCG and HDR with up to 12-bit video • Compatible with ARIB STD-B32 standard • Versatile high-precision pre-processing filters • Preserves color resolution with 4:2:2, 4:4:4, and RGB formats • 22.2-ch AAC audio encoding and decoding SPIN DIGITAL HEVC/H.265 ENCODER Support for the HEVC standard: Main and Main 10 profiles Range Extensions (HEVCv2) profiles ARIB STD-B32 version 3.9 Resolutions: 4K, 8K, and beyond Color formats: 4:2:0, 4:2:2, 4:4:4, RGB Bit depths: 8-, 10-, 12-bit Color spaces: BT.601, BT.709, DCI-P3, BT.2020 HDR support: ST2084 transfer function, ST2086 HDR metadata, HLG Coding
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												  Numerical Behavior of NVIDIA Tensor CoresNumerical behavior of NVIDIA tensor cores Massimiliano Fasi1, Nicholas J. Higham2, Mantas Mikaitis2 and Srikara Pranesh2 1 School of Science and Technology, Örebro University, Örebro, Sweden 2 Department of Mathematics, University of Manchester, Manchester, UK ABSTRACT We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are hardware accelerators for mixed-precision matrix multiplication available on the Volta, Turing, and Ampere microarchitectures. Using Volta V100, Turing T4, and Ampere A100 graphics cards, we determine what precision is used for the intermediate results, whether subnormal numbers are supported, what rounding mode is used, in which order the operations underlying the matrix multiplication are performed, and whether partial sums are normalized. These aspects are not documented by NVIDIA, and we gain insight by running carefully designed numerical experiments on these hardware units. Knowing the answers to these questions is important if one wishes to: (1) accurately simulate NVIDIA tensor cores on conventional hardware; (2) understand the differences between results produced by code that utilizes tensor cores and code that uses only IEEE 754-compliant arithmetic operations; and (3) build custom hardware whose behavior matches that of NVIDIA tensor cores. As part of this work we provide a test suite that can be easily adapted to test newer versions of the NVIDIA tensorcoresaswellassimilaracceleratorsfromothervendors,astheybecome available. Moreover, we identify a non-monotonicity issue
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												  Manycore GPU Architectures and Programming, Part 1Lecture 19: Manycore GPU Architectures and Programming, Part 1 Concurrent and Mul=core Programming CSE 436/536, [email protected] www.secs.oakland.edu/~yan 1 Topics (Part 2) • Parallel architectures and hardware – Parallel computer architectures – Memory hierarchy and cache coherency • Manycore GPU architectures and programming – GPUs architectures – CUDA programming – Introduc?on to offloading model in OpenMP and OpenACC • Programming on large scale systems (Chapter 6) – MPI (point to point and collec=ves) – Introduc?on to PGAS languages, UPC and Chapel • Parallel algorithms (Chapter 8,9 &10) – Dense matrix, and sorng 2 Manycore GPU Architectures and Programming: Outline • Introduc?on – GPU architectures, GPGPUs, and CUDA • GPU Execuon model • CUDA Programming model • Working with Memory in CUDA – Global memory, shared and constant memory • Streams and concurrency • CUDA instruc?on intrinsic and library • Performance, profiling, debugging, and error handling • Direc?ve-based high-level programming model – OpenACC and OpenMP 3 Computer Graphics GPU: Graphics Processing Unit 4 Graphics Processing Unit (GPU) Image: h[p://www.ntu.edu.sg/home/ehchua/programming/opengl/CG_BasicsTheory.html 5 Graphics Processing Unit (GPU) • Enriching user visual experience • Delivering energy-efficient compung • Unlocking poten?als of complex apps • Enabling Deeper scien?fic discovery 6 What is GPU Today? • It is a processor op?mized for 2D/3D graphics, video, visual compu?ng, and display. • It is highly parallel, highly multhreaded mulprocessor op?mized for visual
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												  GPU Technology Conference 2010 Sessions on 2095GPU Technology Conference 2010 Sessions on Video Processing (subject to change) IMPORTANT: Visit www.nvidia.com/gtc for the most up-to-date schedule and to enroll into sessions to ensure your spot in the most popular courses. 2095 - Building High Density Real-Time Video Processing Systems Learn how GPU Direct can be used to effectively build real time, high performance, cost effective video processing products. We will focus especially on how to optimize bus throughput while keeping CPU load and latency minimal. Speaker: Ronny Dewaele, Barco Topics: Video Processing, Imaging Time: Thursday, September, 23rd, 16:00 - 16:50 2029 - Computer Vision Algorithms for Automating HD Post- Production Discover how post-production tasks can be accelerated by taking advantage of GPU-based algorithms. In this talk we present computer vision algorithms for corner detection, feature point tracking, image warping and image inpainting, and their efficient implementation on GPUs using CUDA. We also show how to use these algorithms to do real-time stabilization and temporal re-sampling (re-timing) of high definition video sequences, both common tasks in post-production. Benchmarking of the GPU implementations against optimized CPU algorithms demonstrates a speedup of approximately an order of magnitude. Speaker: Hannes Fassold, JOANNEUM RESEARCH Topics: Computer Vision, Video Processing Time: Wednesday, September, 22nd, 15:00 - 15:50 2125 - Developing GPU Enabled Visual Effects For Film And Video 1 The arrival of fully programable GPUs is now changing the visual effects industry, which traditionally relied on CPU computation to create their spectacular imagery. Implementing the complex image processing algorithms used by VFX is a challenge, but the payoffs in terms of interactivity and throughput can be enormous.
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												  Comparison of JPEG's Competitors for Document ImagesComparison of JPEG’s competitors for document images Mostafa Darwiche1, The-Anh Pham1 and Mathieu Delalandre1 1 Laboratoire d’Informatique, 64 Avenue Jean Portalis, 37200 Tours, France e-mail: fi[email protected] Abstract— In this work, we carry out a study on the per- in [6] concerns assessing quality of common image formats formance of potential JPEG’s competitors when applied to (e.g., JPEG, TIFF, and PNG) that relies on optical charac- document images. Many novel codecs, such as BPG, Mozjpeg, ter recognition (OCR) errors and peak signal to noise ratio WebP and JPEG-XR, have been recently introduced in order to substitute the standard JPEG. Nonetheless, there is a lack of (PSNR) metric. The authors in [3] compare the performance performance evaluation of these codecs, especially for a particular of different coding methods (JPEG, JPEG 2000, MRC) using category of document images. Therefore, this work makes an traditional PSNR metric applied to several document samples. attempt to provide a detailed and thorough analysis of the Since our target is to provide a study on document images, aforementioned JPEG’s competitors. To this aim, we first provide we then use a large dataset with different image resolutions, a review of the most famous codecs that have been considered as being JPEG replacements. Next, some experiments are performed compress them at very low bit-rate and after that evaluate the to study the behavior of these coding schemes. Finally, we extract output images using OCR accuracy. We also take into account main remarks and conclusions characterizing the performance the PSNR measure to serve as an additional quality metric.