Technique for Lossless Compression of Color Images Based on Hierarchical Prediction, Inversion, and Context Adaptive Coding
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A General Scheme for Dithering Multidimensional Signals, and a Visual Instance of Encoding Images with Limited Palettes
Journal of King Saud University – Computer and Information Sciences (2014) 26, 202–217 King Saud University Journal of King Saud University – Computer and Information Sciences www.ksu.edu.sa www.sciencedirect.com A General scheme for dithering multidimensional signals, and a visual instance of encoding images with limited palettes Mohamed Attia a,b,c,*,1,2, Waleed Nazih d,3, Mohamed Al-Badrashiny e,4, Hamed Elsimary d,3 a The Engineering Company for the Development of Computer Systems, RDI, Giza, Egypt b Luxor Technology Inc., Oakville, Ontario L6L6V2, Canada c Arab Academy for Science & Technology (AAST), Heliopolis Campus, Cairo, Egypt d College of Computer Engineering and Sciences, Salman bin Abdulaziz University, AlKharj, Saudi Arabia e King Abdul-Aziz City for Science and Technology (KACST), Riyadh, Saudi Arabia Received 12 March 2013; revised 30 August 2013; accepted 5 December 2013 Available online 12 December 2013 KEYWORDS Abstract The core contribution of this paper is to introduce a general neat scheme based on soft Digital signal processing; vector clustering for the dithering of multidimensional signals that works in any space of arbitrary Digital image processing; dimensionality, on arbitrary number and distribution of quantization centroids, and with a comput- Dithering; able and controllable quantization noise. Dithering upon the digitization of one-dimensional and Multidimensional signals; multi-dimensional signals disperses the quantization noise over the frequency domain which renders Quantization noise; it less perceptible by signal processing systems including the human cognitive ones, so it has a very Soft vector clustering beneficial impact on vital domains such as communications, control, machine-learning, etc. -
An Effective Self-Adaptive Policy for Optimal Video Quality Over Heterogeneous Mobile Devices and Network Discovery Services
Appl. 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. -
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 -
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 Computer
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 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 -
Source Coding: Part I of Fundamentals of Source and Video Coding
Foundations and Trends R in sample Vol. 1, No 1 (2011) 1–217 c 2011 Thomas Wiegand and Heiko Schwarz DOI: xxxxxx Source Coding: Part I of Fundamentals of Source and Video Coding Thomas Wiegand1 and Heiko Schwarz2 1 Berlin Institute of Technology and Fraunhofer Institute for Telecommunica- tions — Heinrich Hertz Institute, Germany, [email protected] 2 Fraunhofer Institute for Telecommunications — Heinrich Hertz Institute, Germany, [email protected] Abstract Digital media technologies have become an integral part of the way we create, communicate, and consume information. At the core of these technologies are source coding methods that are described in this text. Based on the fundamentals of information and rate distortion theory, the most relevant techniques used in source coding algorithms are de- scribed: entropy coding, quantization as well as predictive and trans- form coding. The emphasis is put onto algorithms that are also used in video coding, which will be described in the other text of this two-part monograph. To our families Contents 1 Introduction 1 1.1 The Communication Problem 3 1.2 Scope and Overview of the Text 4 1.3 The Source Coding Principle 5 2 Random Processes 7 2.1 Probability 8 2.2 Random Variables 9 2.2.1 Continuous Random Variables 10 2.2.2 Discrete Random Variables 11 2.2.3 Expectation 13 2.3 Random Processes 14 2.3.1 Markov Processes 16 2.3.2 Gaussian Processes 18 2.3.3 Gauss-Markov Processes 18 2.4 Summary of Random Processes 19 i ii Contents 3 Lossless Source Coding 20 3.1 Classification -
Spin Digital HEVC/H.265 Software Encoder (Spin Enc) Enables Ultra-High-Quality Video with the Highest Compression Level
Spin 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 -
VM Dissertation 2009
WAVELET BASED IMAGE COMPRESSION INTEGRATING ERROR PROTECTION via ARITHMETIC CODING with FORBIDDEN SYMBOL and MAP METRIC SEQUENTIAL DECODING with ARQ RETRANSMISSION By Veruschia Mahomed BSc. (Electronic Engineering) Submitted in fulfilment of the requirements for the Degree of Master of Science in Electronic Engineering in the School of Electrical, Electronic and Computer Engineering at the University of KwaZulu-Natal, Durban December 2009 Preface The research described in this dissertation was performed at the University of KwaZulu-Natal (Howard College Campus), Durban, over the period July 2005 until January 2007 as a full time dissertation and February 2007 until July 2009 as a part time dissertation by Miss. Veruschia Mahomed under the supervision of Professor Stanley Mneney. This work has been generously sponsored by Armscor and Morwadi. I hereby declare that all the material incorporated in this dissertation is my own original unaided work except where specific acknowledgment is made by name or in the form of a reference. The work contained herein has not been submitted in whole or part for a degree at any other university. Signed : ________________________ Name : Miss. Veruschia Mahomed Date : 30 December 2009 As the candidate’s supervisor I have approved this thesis for submission. Signed : ________________________ Name : Prof. S.H. Mneney Date : ii Acknowledgements First and foremost, I wish to thank my supervisor, Professor Stanley Mneney, for his supervision, encouragement and deep insight during the course of this research and for allowing me to pursue a dissertation in a field of research that I most enjoy. His comments throughout were invaluable, constructive and insightful and his willingness to set aside his time to assist me is most appreciated. -
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Geodesic Image and Video Editing
Geodesic Image and Video Editing ANTONIO CRIMINISI and, TOBY SHARP and, CARSTEN ROTHER Microsoft Research Ltd, CB3 0FB, Cambridge, UK and PATRICK PEREZ´ Technicolor Research and Innovation, F-35576 Cesson-Sevign´ e,´ France This paper presents a new, unified technique to perform general edge- 1. INTRODUCTION AND LITERATURE SURVEY sensitive editing operations on n-dimensional images and videos efficiently. The first contribution of the paper is the introduction of a generalized Recent years have seen an explosion of research in Computational geodesic distance transform (GGDT), based on soft masks. This provides a Photography, with many exciting new techniques been invented to unified framework to address several, edge-aware editing operations. Di- aid users accomplish difficult image and video editing tasks effec- verse tasks such as de-noising and non-photorealistic rendering, are all tively. Much attention has been focused on: segmentation [Boykov dealt with fundamentally the same, fast algorithm. Second, a new, geodesic, and Jolly 2001; Bai and Sapiro 2007; Grady and Sinop 2008; Li symmetric filter (GSF) is presented which imposes contrast-sensitive spa- et al. 2004; Rother et al. 2004; Sinop and Grady 2007; Wang et al. tial smoothness into segmentation and segmentation-based editing tasks 2005], bilateral filtering [Chen et al. 2007; Tomasi and Manduchi (cutout, object highlighting, colorization, panorama stitching). The effect 1998; Weiss 2006] and anisotropic diffusion [Perona and Malik of the filter is controlled by two intuitive, geometric parameters. In contrast 1990], non-photorealistic rendering [Bousseau et al. 2007; Wang to existing techniques, the GSF filter is applied to real-valued pixel likeli- et al. -
Download Special Issue
Wireless Communications and Mobile Computing Error Control Codes for Next-Generation Communication Systems: Opportunities and Challenges Lead Guest Editor: Zesong Fei Guest Editors: Jinhong Yuan and Qin Huang Error Control Codes for Next-Generation Communication Systems: Opportunities and Challenges Wireless Communications and Mobile Computing Error Control Codes for Next-Generation Communication Systems: Opportunities and Challenges Lead Guest Editor: Zesong Fei Guest Editors: Jinhong Yuan and Qin Huang Copyright © 2018 Hindawi. All rights reserved. This is a special issue published in “Wireless Communications and Mobile Computing.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, pro- vided the original work is properly cited. Editorial Board Javier Aguiar, Spain Oscar Esparza, Spain Maode Ma, Singapore Ghufran Ahmed, Pakistan Maria Fazio, Italy Imadeldin Mahgoub, USA Wessam Ajib, Canada Mauro Femminella, Italy Pietro Manzoni, Spain Muhammad Alam, China Manuel Fernandez-Veiga, Spain Álvaro Marco, Spain Eva Antonino-Daviu, Spain Gianluigi Ferrari, Italy Gustavo Marfia, Italy Shlomi Arnon, Israel Ilario Filippini, Italy Francisco J. Martinez, Spain Leyre Azpilicueta, Mexico Jesus Fontecha, Spain Davide Mattera, Italy Paolo Barsocchi, Italy Luca Foschini, Italy Michael McGuire, Canada Alessandro Bazzi, Italy A. G. Fragkiadakis, Greece Nathalie Mitton, France Zdenek Becvar, Czech Republic Sabrina Gaito, Italy Klaus Moessner, UK Francesco Benedetto, Italy Óscar García, Spain Antonella Molinaro, Italy Olivier Berder, France Manuel García Sánchez, Spain Simone Morosi, Italy Ana M. Bernardos, Spain L. J. García Villalba, Spain Kumudu S. Munasinghe, Australia Mauro Biagi, Italy José A. García-Naya, Spain Enrico Natalizio, France Dario Bruneo, Italy Miguel Garcia-Pineda, Spain Keivan Navaie, UK Jun Cai, Canada A.-J. -
Backward Coding of Wavelet Trees with Fine-Grained Bitrate Control
JOURNAL OF COMPUTERS, VOL. 1, NO. 4, JULY 2006 1 Backward Coding of Wavelet Trees with Fine-grained Bitrate Control Jiangling Guo School of Information Science and Technology, Beijing Institute of Technology at Zhuhai, Zhuhai, P.R. China Email: [email protected] Sunanda Mitra, Brian Nutter and Tanja Karp Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, USA Email: {sunanda.mitra, brian.nutter, tanja.karp}@ttu.edu Abstract—Backward Coding of Wavelet Trees (BCWT) is Recently, several new wavelet-tree-based codecs have an extremely fast wavelet-tree-based image coding algo- been developed, such as LTW [7] and Progres [8], which rithm. Utilizing a unique backward coding algorithm, drastically improved the computational efficiency in BCWT also provides a rich set of features such as resolu- terms of coding speed. In [9] we have presented our tion-scalability, extremely low memory usage, and extremely newly developed BCWT codec, which is the fastest low complexity. However, BCWT in its original form inher- its one drawback also existing in most non-bitplane codecs, wavelet-tree-based codec we have studied to date with namely coarse bitrate control. In this paper, two solutions the same compression performance as SPIHT. With its for improving the bitrate controllability of BCWT are pre- unique backward coding, the wavelet coefficients from sented. The first solution is based on dual minimum quanti- high frequency subbands to low frequency subbands, zation levels, allowing BCWT to achieve fine-grained bi- BCWT also provides a rich set of features such as low trates with quality-index as a controlling parameter; the memory usage, low complexity and resolution scalability, second solution is based on both dual minimum quantiza- usually lacking in other wavelet-tree-based codecs.