Implementation of an Mpeg-1 Video Encoder and Decoder
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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 -
Video Codec Requirements and Evaluation Methodology
Video Codec Requirements 47pt 30pt and Evaluation Methodology Color::white : LT Medium Font to be used by customers and : Arial www.huawei.com draft-filippov-netvc-requirements-01 Alexey Filippov, Huawei Technologies 35pt Contents Font to be used by customers and partners : • An overview of applications • Requirements 18pt • Evaluation methodology Font to be used by customers • Conclusions and partners : Slide 2 Page 2 35pt Applications Font to be used by customers and partners : • Internet Protocol Television (IPTV) • Video conferencing 18pt • Video sharing Font to be used by customers • Screencasting and partners : • Game streaming • Video monitoring / surveillance Slide 3 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : • Basic requirements: . Random access to pictures 18pt Random Access Period (RAP) should be kept small enough (approximately, 1-15 seconds); Font to be used by customers . Temporal (frame-rate) scalability; and partners : . Error robustness • Optional requirements: . resolution and quality (SNR) scalability Slide 4 35pt Internet Protocol Television (IPTV) Font to be used by customers and partners : Resolution Frame-rate, fps Picture access mode 2160p (4K),3840x2160 60 RA 18pt 1080p, 1920x1080 24, 50, 60 RA 1080i, 1920x1080 30 (60 fields per second) RA Font to be used by customers and partners : 720p, 1280x720 50, 60 RA 576p (EDTV), 720x576 25, 50 RA 576i (SDTV), 720x576 25, 30 RA 480p (EDTV), 720x480 50, 60 RA 480i (SDTV), 720x480 25, 30 RA Slide 5 35pt Video conferencing Font to be used by customers and partners : • Basic requirements: . Delay should be kept as low as possible 18pt The preferable and maximum delay values should be less than 100 ms and 350 ms, respectively Font to be used by customers . -
Digital Video Quality Handbook (May 2013
Digital Video Quality Handbook May 2013 This page intentionally left blank. Executive Summary Under the direction of the Department of Homeland Security (DHS) Science and Technology Directorate (S&T), First Responders Group (FRG), Office for Interoperability and Compatibility (OIC), the Johns Hopkins University Applied Physics Laboratory (JHU/APL), worked with the Security Industry Association (including Steve Surfaro) and members of the Video Quality in Public Safety (VQiPS) Working Group to develop the May 2013 Video Quality Handbook. This document provides voluntary guidance for providing levels of video quality in public safety applications for network video surveillance. Several video surveillance use cases are presented to help illustrate how to relate video component and system performance to the intended application of video surveillance, while meeting the basic requirements of federal, state, tribal and local government authorities. Characteristics of video surveillance equipment are described in terms of how they may influence the design of video surveillance systems. In order for the video surveillance system to meet the needs of the user, the technology provider must consider the following factors that impact video quality: 1) Device categories; 2) Component and system performance level; 3) Verification of intended use; 4) Component and system performance specification; and 5) Best fit and link to use case(s). An appendix is also provided that presents content related to topics not covered in the original document (especially information related to video standards) and to update the material as needed to reflect innovation and changes in the video environment. The emphasis is on the implications of digital video data being exchanged across networks with large numbers of components or participants. -
The Discrete Cosine Transform (DCT): Theory and Application
The Discrete Cosine Transform (DCT): 1 Theory and Application Syed Ali Khayam Department of Electrical & Computer Engineering Michigan State University March 10th 2003 1 This document is intended to be tutorial in nature. No prior knowledge of image processing concepts is assumed. Interested readers should follow the references for advanced material on DCT. ECE 802 – 602: Information Theory and Coding Seminar 1 – The Discrete Cosine Transform: Theory and Application 1. Introduction Transform coding constitutes an integral component of contemporary image/video processing applications. Transform coding relies on the premise that pixels in an image exhibit a certain level of correlation with their neighboring pixels. Similarly in a video transmission system, adjacent pixels in consecutive frames2 show very high correlation. Consequently, these correlations can be exploited to predict the value of a pixel from its respective neighbors. A transformation is, therefore, defined to map this spatial (correlated) data into transformed (uncorrelated) coefficients. Clearly, the transformation should utilize the fact that the information content of an individual pixel is relatively small i.e., to a large extent visual contribution of a pixel can be predicted using its neighbors. A typical image/video transmission system is outlined in Figure 1. The objective of the source encoder is to exploit the redundancies in image data to provide compression. In other words, the source encoder reduces the entropy, which in our case means decrease in the average number of bits required to represent the image. On the contrary, the channel encoder adds redundancy to the output of the source encoder in order to enhance the reliability of the transmission. -
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. -
On the Accuracy of Video Quality Measurement Techniques
On the accuracy of video quality measurement techniques Deepthi Nandakumar Yongjun Wu Hai Wei Avisar Ten-Ami Amazon Video, Bangalore, India Amazon Video, Seattle, USA Amazon Video, Seattle, USA Amazon Video, Seattle, USA, [email protected] [email protected] [email protected] [email protected] Abstract —With the massive growth of Internet video and therefore measuring and optimizing for video quality in streaming, it is critical to accurately measure video quality these high quality ranges is an imperative for streaming service subjectively and objectively, especially HD and UHD video which providers. In this study, we lay specific emphasis on the is bandwidth intensive. We summarize the creation of a database measurement accuracy of subjective and objective video quality of 200 clips, with 20 unique sources tested across a variety of scores in this high quality range. devices. By classifying the test videos into 2 distinct quality regions SD and HD, we show that the high correlation claimed by objective Globally, a healthy mix of devices with different screen sizes video quality metrics is led mostly by videos in the SD quality and form factors is projected to contribute to IP traffic in 2021 region. We perform detailed correlation analysis and statistical [1], ranging from smartphones (44%), to tablets (6%), PCs hypothesis testing of the HD subjective quality scores, and (19%) and TVs (24%). It is therefore necessary for streaming establish that the commonly used ACR methodology of subjective service providers to quantify the viewing experience based on testing is unable to capture significant quality differences, leading the device, and possibly optimize the encoding and delivery to poor measurement accuracy for both subjective and objective process accordingly. -
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 -
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.