Video Codec Requirements and Evaluation Methodology
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Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1 -
Avid Supported Video File Formats
Avid Supported Video File Formats 04.07.2021 Page 1 Avid Supported Video File Formats 4/7/2021 Table of Contents Common Industry Formats ............................................................................................................................................................................................................................................................................................................................................................................................... 4 Application & Device-Generated Formats .................................................................................................................................................................................................................................................................................................................................................................. 8 Stereoscopic 3D Video Formats ...................................................................................................................................................................................................................................................................................................................................................................................... 11 Quick Lookup of Common File Formats ARRI..............................................................................................................................................................................................................................................................................................................................................................4 -
Lecture 11 : Discrete Cosine Transform Moving Into the Frequency Domain
Lecture 11 : Discrete Cosine Transform Moving into the Frequency Domain Frequency domains can be obtained through the transformation from one (time or spatial) domain to the other (frequency) via Fourier Transform (FT) (see Lecture 3) — MPEG Audio. Discrete Cosine Transform (DCT) (new ) — Heart of JPEG and MPEG Video, MPEG Audio. Note : We mention some image (and video) examples in this section with DCT (in particular) but also the FT is commonly applied to filter multimedia data. External Link: MIT OCW 8.03 Lecture 11 Fourier Analysis Video Recap: Fourier Transform The tool which converts a spatial (real space) description of audio/image data into one in terms of its frequency components is called the Fourier transform. The new version is usually referred to as the Fourier space description of the data. We then essentially process the data: E.g . for filtering basically this means attenuating or setting certain frequencies to zero We then need to convert data back to real audio/imagery to use in our applications. The corresponding inverse transformation which turns a Fourier space description back into a real space one is called the inverse Fourier transform. What do Frequencies Mean in an Image? Large values at high frequency components mean the data is changing rapidly on a short distance scale. E.g .: a page of small font text, brick wall, vegetation. Large low frequency components then the large scale features of the picture are more important. E.g . a single fairly simple object which occupies most of the image. The Road to Compression How do we achieve compression? Low pass filter — ignore high frequency noise components Only store lower frequency components High pass filter — spot gradual changes If changes are too low/slow — eye does not respond so ignore? Low Pass Image Compression Example MATLAB demo, dctdemo.m, (uses DCT) to Load an image Low pass filter in frequency (DCT) space Tune compression via a single slider value n to select coefficients Inverse DCT, subtract input and filtered image to see compression artefacts. -
(A/V Codecs) REDCODE RAW (.R3D) ARRIRAW
What is a Codec? Codec is a portmanteau of either "Compressor-Decompressor" or "Coder-Decoder," which describes a device or program capable of performing transformations on a data stream or signal. Codecs encode a stream or signal for transmission, storage or encryption and decode it for viewing or editing. Codecs are often used in videoconferencing and streaming media solutions. A video codec converts analog video signals from a video camera into digital signals for transmission. It then converts the digital signals back to analog for display. An audio codec converts analog audio signals from a microphone into digital signals for transmission. It then converts the digital signals back to analog for playing. The raw encoded form of audio and video data is often called essence, to distinguish it from the metadata information that together make up the information content of the stream and any "wrapper" data that is then added to aid access to or improve the robustness of the stream. Most codecs are lossy, in order to get a reasonably small file size. There are lossless codecs as well, but for most purposes the almost imperceptible increase in quality is not worth the considerable increase in data size. The main exception is if the data will undergo more processing in the future, in which case the repeated lossy encoding would damage the eventual quality too much. Many multimedia data streams need to contain both audio and video data, and often some form of metadata that permits synchronization of the audio and video. Each of these three streams may be handled by different programs, processes, or hardware; but for the multimedia data stream to be useful in stored or transmitted form, they must be encapsulated together in a container format. -
Image Compression Using Discrete Cosine Transform Method
Qusay Kanaan Kadhim, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.9, September- 2016, pg. 186-192 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 5.258 IJCSMC, Vol. 5, Issue. 9, September 2016, pg.186 – 192 Image Compression Using Discrete Cosine Transform Method Qusay Kanaan Kadhim Al-Yarmook University College / Computer Science Department, Iraq [email protected] ABSTRACT: The processing of digital images took a wide importance in the knowledge field in the last decades ago due to the rapid development in the communication techniques and the need to find and develop methods assist in enhancing and exploiting the image information. The field of digital images compression becomes an important field of digital images processing fields due to the need to exploit the available storage space as much as possible and reduce the time required to transmit the image. Baseline JPEG Standard technique is used in compression of images with 8-bit color depth. Basically, this scheme consists of seven operations which are the sampling, the partitioning, the transform, the quantization, the entropy coding and Huffman coding. First, the sampling process is used to reduce the size of the image and the number bits required to represent it. Next, the partitioning process is applied to the image to get (8×8) image block. Then, the discrete cosine transform is used to transform the image block data from spatial domain to frequency domain to make the data easy to process. -
MC14SM5567 PCM Codec-Filter
Product Preview Freescale Semiconductor, Inc. MC14SM5567/D Rev. 0, 4/2002 MC14SM5567 PCM Codec-Filter The MC14SM5567 is a per channel PCM Codec-Filter, designed to operate in both synchronous and asynchronous applications. This device 20 performs the voice digitization and reconstruction as well as the band 1 limiting and smoothing required for (A-Law) PCM systems. DW SUFFIX This device has an input operational amplifier whose output is the input SOG PACKAGE CASE 751D to the encoder section. The encoder section immediately low-pass filters the analog signal with an RC filter to eliminate very-high-frequency noise from being modulated down to the pass band by the switched capacitor filter. From the active R-C filter, the analog signal is converted to a differential signal. From this point, all analog signal processing is done differentially. This allows processing of an analog signal that is twice the amplitude allowed by a single-ended design, which reduces the significance of noise to both the inverted and non-inverted signal paths. Another advantage of this differential design is that noise injected via the power supplies is a common mode signal that is cancelled when the inverted and non-inverted signals are recombined. This dramatically improves the power supply rejection ratio. After the differential converter, a differential switched capacitor filter band passes the analog signal from 200 Hz to 3400 Hz before the signal is digitized by the differential compressing A/D converter. The decoder accepts PCM data and expands it using a differential D/A converter. The output of the D/A is low-pass filtered at 3400 Hz and sinX/X compensated by a differential switched capacitor filter. -
VIDEO Blu-Ray™ Disc Player BP330
VIDEO Blu-ray™ Disc Player BP330 Internet access lets you stream instant content from Make the most of your HDTV. Blu-ray disc playback Less clutter. More possibilities. Cut loose from Netflix, CinemaNow, Vudu and YouTube direct to delivers exceptional Full HD 1080p video messy wires. Integrated Wi-Fi® connectivity allows your TV — no computer required. performance, along with Bonus-view for a picture-in- you take advantage of Internet access from any picture. available Wi-Fi® connection in its range. VIDEO Blu-ray™ Disc Player BP330 PROFILE & PLAYABLE DISC PLAYABLE AUDIO FORMATS BD Profile 2.0 LPCM Yes USB Playback Yes Dolby® Digital Yes External HDD Playback Yes (via USB) Dolby® Digital Plus Yes BD-ROM/BD-R/BD-RE Yes Dolby® TrueHD Yes DVD-ROM/DVD±R/DVD±RW Yes DTS Yes Audio CD/CD-R/CD-RW Yes DTS-HD Master Audio Yes DTS-CD Yes MPEG 1/2 L2 Yes MP3 Yes LG SMART TV WMA Yes Premium Content Yes AAC Yes Netflix® Yes FLAC Yes YouTube® Yes Amazon® Yes PLAYABLE PHOTO FORMATS Hulu Plus® Yes JPEG Yes Vudu® Yes GIF/Animated GIF Yes CinemaNow® Yes PNG Yes Pandora® Yes MPO Yes Picasa® Yes AccuWeather® Yes CONVENIENCE SIMPLINK™ Yes VIDEO FEATURES Loading Time >10 Sec 1080p Up-scaling Yes LG Remote App Yes (Free download on Google Play and Apple App Store) Noise Reduction Yes Last Scene Memory Yes Deep Color Yes Screen Saver Yes NvYCC Yes Auto Power Off Yes Video Enhancement Yes Parental Lock Yes Yes Yes CONNECTIVITY Wired LAN Yes AUDIO FEATURES Wi-Fi® Built-in Yes Dolby Digital® Down Mix Yes DLNA Certified® Yes Re-Encoder Yes (DTS only) LPCM Conversion -
Lossless Compression of Audio Data
CHAPTER 12 Lossless Compression of Audio Data ROBERT C. MAHER OVERVIEW Lossless data compression of digital audio signals is useful when it is necessary to minimize the storage space or transmission bandwidth of audio data while still maintaining archival quality. Available techniques for lossless audio compression, or lossless audio packing, generally employ an adaptive waveform predictor with a variable-rate entropy coding of the residual, such as Huffman or Golomb-Rice coding. The amount of data compression can vary considerably from one audio waveform to another, but ratios of less than 3 are typical. Several freeware, shareware, and proprietary commercial lossless audio packing programs are available. 12.1 INTRODUCTION The Internet is increasingly being used as a means to deliver audio content to end-users for en tertainment, education, and commerce. It is clearly advantageous to minimize the time required to download an audio data file and the storage capacity required to hold it. Moreover, the expec tations of end-users with regard to signal quality, number of audio channels, meta-data such as song lyrics, and similar additional features provide incentives to compress the audio data. 12.1.1 Background In the past decade there have been significant breakthroughs in audio data compression using lossy perceptual coding [1]. These techniques lower the bit rate required to represent the signal by establishing perceptual error criteria, meaning that a model of human hearing perception is Copyright 2003. Elsevier Science (USA). 255 AU rights reserved. 256 PART III / APPLICATIONS used to guide the elimination of excess bits that can be either reconstructed (redundancy in the signal) orignored (inaudible components in the signal). -
Netflix and the Development of the Internet Television Network
Syracuse University SURFACE Dissertations - ALL SURFACE May 2016 Netflix and the Development of the Internet Television Network Laura Osur Syracuse University Follow this and additional works at: https://surface.syr.edu/etd Part of the Social and Behavioral Sciences Commons Recommended Citation Osur, Laura, "Netflix and the Development of the Internet Television Network" (2016). Dissertations - ALL. 448. https://surface.syr.edu/etd/448 This Dissertation is brought to you for free and open access by the SURFACE at SURFACE. It has been accepted for inclusion in Dissertations - ALL by an authorized administrator of SURFACE. For more information, please contact [email protected]. Abstract When Netflix launched in April 1998, Internet video was in its infancy. Eighteen years later, Netflix has developed into the first truly global Internet TV network. Many books have been written about the five broadcast networks – NBC, CBS, ABC, Fox, and the CW – and many about the major cable networks – HBO, CNN, MTV, Nickelodeon, just to name a few – and this is the fitting time to undertake a detailed analysis of how Netflix, as the preeminent Internet TV networks, has come to be. This book, then, combines historical, industrial, and textual analysis to investigate, contextualize, and historicize Netflix's development as an Internet TV network. The book is split into four chapters. The first explores the ways in which Netflix's development during its early years a DVD-by-mail company – 1998-2007, a period I am calling "Netflix as Rental Company" – lay the foundations for the company's future iterations and successes. During this period, Netflix adapted DVD distribution to the Internet, revolutionizing the way viewers receive, watch, and choose content, and built a brand reputation on consumer-centric innovation. -
The H.264 Advanced Video Coding (AVC) Standard
Whitepaper: The H.264 Advanced Video Coding (AVC) Standard What It Means to Web Camera Performance Introduction A new generation of webcams is hitting the market that makes video conferencing a more lifelike experience for users, thanks to adoption of the breakthrough H.264 standard. This white paper explains some of the key benefits of H.264 encoding and why cameras with this technology should be on the shopping list of every business. The Need for Compression Today, Internet connection rates average in the range of a few megabits per second. While VGA video requires 147 megabits per second (Mbps) of data, full high definition (HD) 1080p video requires almost one gigabit per second of data, as illustrated in Table 1. Table 1. Display Resolution Format Comparison Format Horizontal Pixels Vertical Lines Pixels Megabits per second (Mbps) QVGA 320 240 76,800 37 VGA 640 480 307,200 147 720p 1280 720 921,600 442 1080p 1920 1080 2,073,600 995 Video Compression Techniques Digital video streams, especially at high definition (HD) resolution, represent huge amounts of data. In order to achieve real-time HD resolution over typical Internet connection bandwidths, video compression is required. The amount of compression required to transmit 1080p video over a three megabits per second link is 332:1! Video compression techniques use mathematical algorithms to reduce the amount of data needed to transmit or store video. Lossless Compression Lossless compression changes how data is stored without resulting in any loss of information. Zip files are losslessly compressed so that when they are unzipped, the original files are recovered. -
Frequently Asked Questions Dolby Digital Plus
Frequently Asked Questions Dolby® Digital Plus redefines the home theater surround experience for new formats like high-definition video discs. What is Dolby Digital Plus? Dolby® Digital Plus is Dolby’s new-generation multichannel audio technology developed to enhance the premium experience of high-definition media. Built on industry-standard Dolby Digital technology, Dolby Digital Plus as implemented in Blu-ray Disc™ features more channels, less compression, and higher data rates for a warmer, richer, more compelling audio experience than you get from standard-definition DVDs. What other applications are there for Dolby Digital Plus? The advanced spectral coding efficiencies of Dolby Digital Plus enable content producers to deliver high-resolution multichannel soundtracks at lower bit rates than with Dolby Digital. This makes it ideal for emerging bandwidth-critical applications including cable, IPTV, IP streaming, and satellite (DBS) and terrestrial broadcast. Dolby Digital Plus is also a preferred medium for delivering BonusView (Profile 1.1) and BD-Live™ (Profile 2.0) interactive audio content on Blu-ray Disc. Delivering higher quality and more channels at higher bit rates, plus greater efficiency at lower bit rates, Dolby Digital Plus has the flexibility to fulfill the needs of new content delivery formats for years to come. Is Dolby Digital Plus content backward-compatible? Because Dolby Digital Plus is built on core Dolby Digital technologies, content that is encoded with Dolby Digital Plus is fully compatible with the millions of existing home theaters and playback systems worldwide equipped for Dolby Digital playback. Dolby Digital Plus soundtracks are easily converted to a 640 kbps Dolby Digital signal without decoding and reencoding, for output via S/PDIF. -
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter Prediction using Binary classification techniques A graduate project submitted in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering by Alex Kit Romero May 2020 The graduate project of Alex Kit Romero is approved: ____________________________________ ____________ Dr. Katya Mkrtchyan Date ____________________________________ ____________ Dr. Kyle Dewey Date ____________________________________ ____________ Dr. John J. Noga, Chair Date California State University, Northridge ii Dedication This project is dedicated to all of the Computer Science professors that I have come in contact with other the years who have inspired and encouraged me to pursue a career in computer science. The words and wisdom of these professors are what pushed me to try harder and accomplish more than I ever thought possible. I would like to give a big thanks to the open source community and my fellow cohort of computer science co-workers for always being there with answers to my numerous questions and inquiries. Without their guidance and expertise, I could not have been successful. Lastly, I would like to thank my friends and family who have supported and uplifted me throughout the years. Thank you for believing in me and always telling me to never give up. iii Table of Contents Signature Page ................................................................................................................................ ii Dedication .....................................................................................................................................