Video Encoding Settings for H.264 Excellence
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Download Media Player Codec Pack Version 4.1 Media Player Codec Pack
download 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. -
Encoding H.264 Video for Streaming and Progressive Download
What 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 -
X-Men -Dark-Phoenix-Fact-Sheet-1
THE X-MEN’S GREATEST BATTLE WILL CHANGE THEIR FUTURE! Complete Your X-Men Collection When X-MEN: DARK PHOENIX Arrives on Digital September 3 and 4K Ultra HD™, Blu-ray™ and DVD September 17 X-MEN: DARK PHOENIX Sophie Turner, James McAvoy, Michael Fassbender and Jennifer Lawrence fire up an all-star cast in this spectacular culmination of the X-Men saga! During a rescue mission in space, Jean Grey (Turner) is transformed into the infinitely powerful and dangerous DARK PHOENIX. As Jean spirals out of control, the X-Men must unite to face their most devastating enemy yet — one of their own. The home entertainment release comes packed with hours of extensive special features and behind- the-scenes insights from Simon Kinberg and Hutch Parker delving into everything it took to bring X-MEN: DARK PHOENIX to the big screen. Beast also offers a hilarious, but important, one-on-one “How to Fly Your Jet to Space” lesson in the Special Features section. Check out a clip of the top-notch class session below! Add X-MEN: DARK PHOENIX to your digital collection on Movies Anywhere September 3 and buy it on 4K Ultra HDTM, Blu-rayTM and DVD September 17. X-MEN: DARK PHOENIX 4K Ultra HD, Blu-ray and Digital HD Special Features: ● Deleted Scenes with Optional Commentary by Simon Kinberg and Hutch Parker*: ○ Edwards Air Force Base ○ Charles Returns Home ○ Mission Prep ○ Beast MIA ○ Charles Says Goodbye ● Rise of the Phoenix: The Making of Dark Phoenix (5-Part Documentary) ● Scene Breakdown: The 5th Avenue Sequence** ● How to Fly Your Jet to Space with Beast ● Audio Commentary by Simon Kinberg and Hutch Parker *Commentary available on Blu-ray, iTunes Extras and Movies Anywhere only **Available on Digital only X-MEN: DARK PHOENIX 4K Ultra HD™ Technical Specifications: Street Date: September 17, 2019 Screen Format: Widescreen 16:9 (2.39:1) Audio: English Dolby Atmos, English Descriptive Audio Dolby Digital 5.1, Spanish Dolby Digital 5.1, French DTS 5.1 Subtitles: English for the Deaf and Hard of Hearing, Spanish, French Total Run Time: 114 minutes U.S. -
Challenges in Relaying Video Back to Mission Control
Challenges in Relaying Video Back to Mission Control CONTENTS 1 Introduction ..................................................................................................................................................................................... 2 2 Encoding ........................................................................................................................................................................................... 3 3 Time is of the Essence .............................................................................................................................................................. 3 4 The Reality ....................................................................................................................................................................................... 5 5 The Tradeoff .................................................................................................................................................................................... 5 6 Alleviations and Implementation ......................................................................................................................................... 6 7 Conclusion ....................................................................................................................................................................................... 7 White Paper Revision 2 July 2020 By Christopher Fadeley Using a customizable hardware-accelerated encoder is essential to delivering the high -
Hikvision H.264+ Encoding Technology
WHITE PAPER Hikvision H.264+ Encoding Technology Encoding Improvement / Higher Transmission / Efficiency Storage Savings 2 Contents 1. Introduction .............................................................................................. 3 2. Background ............................................................................................... 3 3. Key Technologies .................................................................................... 4 3.1 Predictive Encoding ........................................................................ 4 3.2 Noise Suppression.......................................................................... 8 3.3 Long-Term Bitrate Control........................................................... 9 4. Applications ............................................................................................ 11 5. Conclusion............................................................................................... 11 Hikvision H.264+ Encoding Technology 3 1. INTRODUCTION As the global market leader in video surveillance products, Hikvision Digital Technology Co., Ltd., continues to strive for enhancement of its products through application of the latest in technology. H.264+ Advanced Video Coding (AVC) optimizes compression beyond the current H.264 standard. Through the combination of intelligent analysis technology with predictive encoding, noise suppression, and long-term bitrate control, Hikvision is meeting the demand for higher resolution at reduced bandwidths. Our customers will benefit -
EMA Mezzanine File Creation Specification and Best Practices Version 1.0.1 For
16530 Ventura Blvd., Suite 400 Encino, CA 91436 818.385.1500 www.entmerch.org EMA Mezzanine File Creation Specification and Best Practices Version 1.0.1 for Digital Audio‐Visual Distribution January 7, 2014 EMA MEZZANINE FILE CREATION SPECIFICATION AND BEST PRACTICES The Mezzanine File Working Group of EMA’s Digital Supply Chain Committee developed the attached recommended Mezzanine File Specification and Best Practices. Why is the Specification and Best Practices document needed? At the request of their customers, content providers and post‐house have been creating mezzanine files unique to each of their retail partners. This causes unnecessary costs in the supply chain and constrains the flow of new content. There is a demand to make more content available for digital distribution more quickly. Sales are lost if content isn’t available to be merchandised. Today’s ecosystem is too manual. Standardization will facilitate automation, reducing costs and increasing speed. Quality control issues slow down today’s processes. Creating one standard mezzanine file instead of many files for the same content should reduce the quantity of errors. And, when an error does occur and is caught by a single customer, it can be corrected for all retailers/distributors. Mezzanine File Working Group Participants in the Mezzanine File Working Group were: Amazon – Ben Waggoner, Ryan Wernet Dish – Timothy Loveridge Google – Bill Kotzman, Doug Stallard Microsoft – Andy Rosen Netflix – Steven Kang , Nick Levin, Chris Fetner Redbox Instant – Joe Ambeault Rovi -
On Engagement with ICT Standards and Their Implementations in Open Source Software Projects: Experiences and Insights from the Multimedia Field
International Journal of Standardization Research Volume 19 • Issue 1 On Engagement With ICT Standards and Their Implementations in Open Source Software Projects: Experiences and Insights From the Multimedia Field Jonas Gamalielsson, University of Skövde, Sweden Björn Lundell, University of Skövde, Sweden ABSTRACT The overarching goal in this paper is to investigate organisational engagement with an ICT standard and open source software (OSS) projects that implement the standard, with a specific focus on the multimedia field, which is relevant in light of the wide deployment of standards and different legal challenges in this field. The first part reports on experiences and insights from engagement with standards in the multimedia field and from implementation of such standards in OSS projects. The second part focuses on the case of the ITU-T H.264 standard and the two OSS projects OpenH264 and x264 that implement the standard, and reports on a characterisation of organisations that engage with and control the H.264 standard, and organisations that engage with and control OSS projects implementing the H.264 standard. Further, projects for standardisation and implementation of H.264 are contrasted with respect to mix of contributing organisations, and findings are related to organisational strategies of contributing organisations and previous research. KEywordS AVC, H.264, Involvement, ISO, ITU-T, OpenH264, Participation, x264 1 INTROdUCTION There are a number of different challenges related to provision of standards in the software sector, that can impact on the extent to which it is possible to faithfully implement the specification of a standard in software systems (Blind and Böhm, 2019; Gamalielsson and Lundell, 2013; Lundell et al., 2019; UK, 2015). -
Full HD Camcorder with Wifi, Built-In Multi Scene Twin Camera and 50X Stabilized Optical Zoom HC-W580K HC-W580K
Full HD Camcorder with WiFi, Built-in Multi Scene Twin Camera and 50x Stabilized Optical Zoom HC-W580K HC-W580K SENSOR SECTION SENSOR FOR MAIN CAMERA Image Sensor 1/5.8-inch BSI MOS Sensor Total Pixels 2.51 megapixels Effective Pixels Motion Image 2.20 megapixels [16:9] (O.I.S. Standard Mode, Level Shot Function OFF) Still Image 1.67 megapixels [3:2] 2.20 megapixels [16:9 1.70 megapixels [4:3] SENSOR FOR SUB CAMERA Image Sensor Total Pixels 2 megapixels LENS SECTION LENS FOR MAIN CAMERA F Value F1.8 (WIDE) / F4.2 (TELE) Optical Zoom 50x Focal Length 2.06 – 103 mm 35 mm Film Camera Equivalent Motion Image (16:9) 28.0 - 1740 mm [16:9] (O.I.S. Standard Mode, Level Shot Function OFF) Still Image 33.6 – 1714 mm [3:2] 28.0 – 1740 mm [16:9] 34.0 – 1766 mm [4:3] Filter Diameter – Lens Brand Panasonic Lens LENS FOR SUB CAMERA F Value F2.8 Focal Length 2.48 mm 35 mm Film Camera Equivalent Motion Image 30.6 mm CAMERA SECTION Standard Illumination 1400 lx Minimum Illumination 4 lx (Scene Mode Low Light 1/30), 1 lx (Night Mode (Color)) Focus Auto / Manual Zoom Intelligent Zoom OFF 62x (O.I.S. Standard Mode, Level Shot Function OFF) Intelligent Zoom ON 90 x (O.I.S. Standard Mode, Level Shot Function: OFF) Digital Zoom 150x / 3000x (The maximum value of zoom magnification can be set in two patterns) White Balance Auto / White Set / Sunny / Cloudy / Indoor1 / Indoor2 Shutter Speed Motion Image Auto Slow Shutter ON : 1/30 – 1/8000 Auto Slow Shutter OFF : 1/60 – 1/8000 Still Image 1/2 – 1/2000 Iris Auto / Manual Image Stabilizer HYBRID O.I.S.+ with Active Mode, O.I.S. -
University of Texas at Arlington Dissertation Template
LOW COMPLEXITY ENCODER USING MACHINE LEARNING by THEJASWINI PURUSHOTHAM 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 December 2010 Copyright © by Thejaswini Purushotham 2010 All Rights Reserved ACKNOWLEDGEMENTS Multitudes of pixels come together to form a lovely portrait. Similarly, the fruition of a thesis happens because of the encouragement and guidance of numerous people. Thus, I would like to take this opportunity to thank everyone who invested their precious time on me in the last two years. In the fall of 2008, I walked into the room of Dr.K.R. Rao with the hopes of learning from the master of video coding. Though we were total strangers, he immediately put me at ease by creating a very positive working atmosphere which entails my sincere appreciation. His mentoring has undoubtedly had a profound impact on me. I am greatly indebted to him. I am deeply grateful to Dr. Dongil Han for always being available in the lab and providing me with continued financial support and technical advice. I would like to thank the other members of my advisory committee Dr. W. Alan Davis and Dr. Jonathan Bredow for reviewing this thesis document and offering insightful comments. My sincere thanks to Pragnesh and Suchethan. The love, affection and encouragement of Bhumika, Bhavana, Gunpreet, Thara, Srikanth and all my friends who kept me going through the trying times of my Masters. Finally, my sincere gratitude and love goes out to my mom Ms. -
Encoding Time Optimization for Intra-Frame Reconstruction Schemes for H.264
International Journal of Engineering & Technology, 7 (2.33) (2018) 245-251 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET Research paper Encoding time optimization for intra-frame reconstruction schemes for H.264 Pradeep Kumar N. S 1.*, H. N. Suresh 2 1 Associate Professor.: Department of Electronics & Communication Engineering, South East Asian College of Engeening & Technology, Bangalore, India 2 Professor Department of Electronics & Instrumentation Engineering Bangalore Institute of Technology, Bangaloe, India *Corresponding author E-mail: [email protected] Abstract The area of transmission compression is gaining a quick momentum by evolving with completely different varieties of compression pro- tocols and coding method. However, majority of the prevailing communication devices still uses H.264 as a customary compression pro- tocol. we tend to reviewed a number of the prevailing system touching on usage of H.264 in video compression to explore it doesn’t sup- ply process effectiveness though it's going to supply higher reconstructed knowledge on the receiver finish. This paper introduces A dis- tinctive optimisation mechanism to optimize the coding time by introducing a value perform formulation. The bestowed design runs over typical H.264 and supply worth additional advantage by creating it additional resource friendly. The rule made uses MPEG file as AN input that endure the method of optimisation of coding time used for coding I and P frame. The study outcome of the projected system is found to supply a much better reduction in coding time as compared to existing mechanism of Lagrangian price perform. As a contribu- tion, our outcome shows a much better equilibrium between the info quality of reconstructed signal and therefore the coding time. -
A Packetization and Variable Bitrate Interframe Compression Scheme for Vector Quantizer-Based Distributed Speech Recognition
A Packetization and Variable Bitrate Interframe Compression Scheme For Vector Quantizer-Based Distributed Speech Recognition Bengt J. Borgstrom¨ and Abeer Alwan Department of Electrical Engineering, University of California, Los Angeles [email protected], [email protected] Abstract For example, the ETSI standard [2] packetization scheme sim- ply concatenates 2 adjacent 44-bit source coded frames, and We propose a novel packetization and variable bitrate com- transmits the resulting signal along with header information. pression scheme for DSR source coding, based on the Group The proposed algorithm performs further compression using of Pictures concept from video coding. The proposed algorithm interframe correlation. The high time correlation present in simultaneously packetizes and further compresses source coded speech has previously been studied for speech coding [3] and features using the high interframe correlation of speech, and is DSR coding [4]. compatible with a variety of VQ-based DSR source coders. The The proposed algorithm allows for lossless compression of algorithm approximates vector quantizers as Markov Chains, the quantized speech features. However, the algorithm is also and empirically trains the corresponding probability parame- robust to various degrees of lossy compression, either through ters. Feature frames are then compressed as I-frames, P-frames, VQ pruning or frame puncturing. VQ pruning refers to ex- or B-frames, using Huffman tables. The proposed scheme can clusion of low probability VQ codebook labels prior to Huff- perform lossless compression, but is also robust to lossy com- man coding, and thus excludes longer Huffman codewords, pression through VQ pruning or frame puncturing. To illustrate and frame puncturing refers to the non-transmission of certain its effectiveness, we applied the proposed algorithm to the ETSI frames, which drastically reduces the final bitrate. -
Bosch Intelligent Streaming
Intelligent streaming | Bitrate-optimized high-quality video 1 | 22 Intelligent streaming Bitrate-optimized high-quality video – White Paper Data subject to change without notice | May 31st, 2019 BT-SC Intelligent streaming | Bitrate-optimized high-quality video 2 | 22 Table of contents 1 Introduction 3 2 Basic structures and processes 4 2.1 Image capture process ..................................................................................................................................... 4 2.2 Image content .................................................................................................................................................... 4 2.3 Scene content .................................................................................................................................................... 5 2.4 Human vision ..................................................................................................................................................... 5 2.5 Video compression ........................................................................................................................................... 6 3 Main concepts and components 7 3.1 Image processing .............................................................................................................................................. 7 3.2 VCA ..................................................................................................................................................................... 7 3.3 Smart Encoder