Video Compression: MPEG-4 and Beyond Video Compression: MPEG-4 and Beyond
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Information and Compression Introduction Summary of Course
Information and compression Introduction ■ We will be looking at what information is Information and Compression ■ Methods of measuring it ■ The idea of compression ■ 2 distinct ways of compressing images Paul Cockshott Faraday partnership Summary of Course Agenda ■ At the end of this lecture you should have ■ Encoding systems an idea what information is, its relation to ■ BCD versus decimal order, and why data compression is possible ■ Information efficiency ■ Shannons Measure ■ Chaitin, Kolmogorov Complexity Theory ■ Relation to Goedels theorem Overview Connections ■ Data coms channels bounded ■ Simple encoding example shows different ■ Images take great deal of information in raw densities possible form ■ Shannons information theory deals with ■ Efficient transmission requires denser transmission encoding ■ Chaitin relates this to Turing machines and ■ This requires that we understand what computability information actually is ■ Goedels theorem limits what we know about compression paul 1 Information and compression Vocabulary What is information ■ information, ■ Information measured in bits ■ entropy, ■ Bit equivalent to binary digit ■ compression, ■ Why use binary system not decimal? ■ redundancy, ■ Decimal would seem more natural ■ lossless encoding, ■ Alternatively why not use e, base of natural ■ lossy encoding logarithms instead of 2 or 10 ? Voltage encoding Noise Immunity Binary voltage ■ ■ ■ 5v Digital information BINARY DECIMAL encoded as ranges of ■ 2 values 0,1 ■ 10 values 0..9 continuous variables 1 ■ 1 isolation band ■ -
Music 422 Project Report
Music 422 Project Report Jatin Chowdhury, Arda Sahiner, and Abhipray Sahoo October 10, 2020 1 Introduction We present an audio coder that seeks to improve upon traditional coding methods by implementing block switching, spectral band replication, and gain-shape quantization. Block switching improves on coding of transient sounds. Spectral band replication exploits low sensitivity of human hearing towards high frequencies. It fills in any missing coded high frequency content with information from the low frequencies. We chose to do gain-shape quantization in order to explicitly code the energy of each band, preserving the perceptually important spectral envelope more accurately. 2 Implementation 2.1 Block Switching Block switching, as introduced by Edler [1], allows the audio coder to adaptively change the size of the block being coded. Modern audio coders typically encode frequency lines obtained using the Modified Discrete Cosine Transform (MDCT). Using longer blocks for the MDCT allows forbet- ter spectral resolution, while shorter blocks have better time resolution, due to the time-frequency trade-off known as the Fourier uncertainty principle [2]. Due to their poor time resolution, using long MDCT blocks for an audio coder can result in “pre-echo” when the coder encodes transient sounds. Block switching allows the coder to use long MDCT blocks for normal signals being encoded and use shorter blocks for transient parts of the signal. In our coder, we implement the block switching algorithm introduced in [1]. This algorithm consists of four types of block windows: long, short, start, and stop. Long and short block windows are simple sine windows, as defined in [3]: (( ) ) 1 π w[n] = sin n + (1) 2 N where N is the length of the block. -
An Improved Fast Fractal Image Compression Coding Method
Rev. Téc. Ing. Univ. Zulia. Vol. 39, Nº 9, 241 - 247, 2016 doi:10.21311/001.39.9.32 An Improved Fast Fractal Image Compression Coding Method Haibo Gao, Wenjuan Zeng, Jifeng Chen, Cheng Zhang College of Information Science and Engineering, Hunan International Economics University, Changsha 410205, Hunan, China Abstract The main purpose of image compression is to use as many bits as possible to represent the source image by eliminating the image redundancy with acceptable restoration. Fractal image compression coding has achieved good results in the field of image compression due to its high compression ratio, fast decoding speed as well as independency between decoding image and resolution. However, there is a big problem in this method, i.e. long coding time because there are considerable searches of fractal blocks in the coding. Based on the theoretical foundation of fractal coding and fractal compression algorithm, this paper researches every step of this compression method, including block partition, block search and the representation of affine transformation coefficient, in order to come up with optimization and improvement strategies. The simulation result shows that the algorithm of this paper can achieve excellent effects in coding time and compression ratio while ensuring better image quality. The work on fractal coding in this paper has made certain contributions to the research of fractal coding. Key words: Image Compression, Fractal Theory, Coding and Decoding. 1. INTRODUCTION Image compression is aimed to use as many bytes as possible to represent and transmit the original big image and to have a better quality in the restored image. -
Javascript and the DOM
Javascript and the DOM 1 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento The web architecture with smart browser The web programmer also writes Programs which run on the browser. Which language? Javascript! HTTP Get + params File System Smart browser Server httpd Cgi-bin Internet Query SQL Client process DB Data Evolution 3: execute code also on client! (How ?) Javascript and the DOM 1- Adding dynamic behaviour to HTML 3 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento Example 1: onmouseover, onmouseout <!DOCTYPE html> <html> <head> <title>Dynamic behaviour</title> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> </head> <body> <div onmouseover="this.style.color = 'red'" onmouseout="this.style.color = 'green'"> I can change my colour!</div> </body> </html> JAVASCRIPT The dynamic behaviour is on the client side! (The file can be loaded locally) <body> <div Example 2: onmouseover, onmouseout onmouseover="this.style.background='orange'; this.style.color = 'blue';" onmouseout=" this.innerText='and my text and position too!'; this.style.position='absolute'; this.style.left='100px’; this.style.top='150px'; this.style.borderStyle='ridge'; this.style.borderColor='blue'; this.style.fontSize='24pt';"> I can change my colour... </div> </body > JavaScript is event-based UiEvents: These event objects iherits the properties of the UiEvent: • The FocusEvent • The InputEvent • The KeyboardEvent • The MouseEvent • The TouchEvent • The WheelEvent See https://www.w3schools.com/jsref/obj_uievent.asp Test and Gym JAVASCRIPT HTML HEAD HTML BODY CSS https://www.jdoodle.com/html-css-javascript-online-editor/ Javascript and the DOM 2- Introduction to the language 8 Introduzione alla programmazione web – Marco Ronchetti 2020 – Università di Trento JavaScript History • JavaScript was born as Mocha, then “LiveScript” at the beginning of the 94’s. -
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs Jan P. Klopp Keng-Chi Liu National Taiwan University Taiwan AI Labs [email protected] [email protected] Liang-Gee Chen Shao-Yi Chien National Taiwan University [email protected] [email protected] Abstract Lossy compression optimises the objective Lossy image compression is often limited by the sim- L = R + λD (1) plicity of the chosen loss measure. Recent research sug- gests that generative adversarial networks have the ability where R and D stand for rate and distortion, respectively, to overcome this limitation and serve as a multi-modal loss, and λ controls their weight relative to each other. In prac- especially for textures. Together with learned image com- tice, computational efficiency is another constraint as at pression, these two techniques can be used to great effect least the decoder needs to process high resolutions in real- when relaxing the commonly employed tight measures of time under a limited power envelope, typically necessitating distortion. However, convolutional neural network-based dedicated hardware implementations. Requirements for the algorithms have a large computational footprint. Ideally, encoder are more relaxed, often allowing even offline en- an existing conventional codec should stay in place, ensur- coding without demanding real-time capability. ing faster adoption and adherence to a balanced computa- Recent research has developed along two lines: evolu- tional envelope. tion of exiting coding technologies, such as H264 [41] or As a possible avenue to this goal, we propose and investi- H265 [35], culminating in the most recent AV1 codec, on gate how learned image coding can be used as a surrogate the one hand. -
MSD FATFS Users Guide
Freescale MSD FATFS Users Guide Document Number: MSDFATFSUG Rev. 0 02/2011 How to Reach Us: Home Page: www.freescale.com E-mail: [email protected] USA/Europe or Locations Not Listed: Freescale Semiconductor Technical Information Center, CH370 1300 N. Alma School Road Chandler, Arizona 85224 +1-800-521-6274 or +1-480-768-2130 [email protected] Europe, Middle East, and Africa: Information in this document is provided solely to enable system and Freescale Halbleiter Deutschland GmbH software implementers to use Freescale Semiconductor products. There are Technical Information Center no express or implied copyright licenses granted hereunder to design or Schatzbogen 7 fabricate any integrated circuits or integrated circuits based on the 81829 Muenchen, Germany information in this document. +44 1296 380 456 (English) +46 8 52200080 (English) Freescale Semiconductor reserves the right to make changes without further +49 89 92103 559 (German) notice to any products herein. Freescale Semiconductor makes no warranty, +33 1 69 35 48 48 (French) representation or guarantee regarding the suitability of its products for any particular purpose, nor does Freescale Semiconductor assume any liability [email protected] arising out of the application or use of any product or circuit, and specifically disclaims any and all liability, including without limitation consequential or Japan: incidental damages. “Typical” parameters that may be provided in Freescale Freescale Semiconductor Japan Ltd. Semiconductor data sheets and/or specifications can and do vary in different Headquarters applications and actual performance may vary over time. All operating ARCO Tower 15F parameters, including “Typicals”, must be validated for each customer 1-8-1, Shimo-Meguro, Meguro-ku, application by customer’s technical experts. -
OSLC Architecture Management Specification 3.0 Project Specification Draft 01 17 September 2020
Standards Track Work Product OSLC Architecture Management Specification 3.0 Project Specification Draft 01 17 September 2020 This stage: https://docs.oasis-open-projects.org/oslc-op/am/v3.0/psd01/architecture-management-spec.html (Authoritative) https://docs.oasis-open-projects.org/oslc-op/am/v3.0/psd01/architecture-management-spec.pdf Previous stage: N/A Latest stage: https://docs.oasis-open-projects.org/oslc-op/am/v3.0/architecture-management-spec.html (Authoritative) https://docs.oasis-open-projects.org/oslc-op/am/v3.0/architecture-management-spec.pdf Latest version: https://open-services.net/spec/am/latest Latest editor's draft: https://open-services.net/spec/am/latest-draft Open Project: OASIS Open Services for Lifecycle Collaboration (OSLC) OP Project Chairs: Jim Amsden ([email protected]), IBM Andrii Berezovskyi ([email protected]), KTH Editor: Jim Amsden ([email protected]), IBM Additional components: This specification is one component of a Work Product that also includes: OSLC Architecture Management Version 3.0. Part 1: Specification (this document). architecture-management- spec.html OSLC Architecture Management Version 3.0. Part 2: Vocabulary. architecture-management-vocab.html OSLC Architecture Management Version 3.0. Part 3: Constraints. architecture-management-shapes.html OSLC Architecture Management Version 3.0. Machine Readable Vocabulary Terms. architecture-management- vocab.ttl OSLC Architecture Management Version 3.0. Machine Readable Constraints. architecture-management-shapes.ttl architecture-management-spec Copyright © OASIS Open 2020. All Rights Reserved. 17 September 2020 - Page 1 of 19 Standards Track Work Product Related work: This specification is related to: OSLC Architecture Management Specification Version 2.0. -
Chapter 9 Image Compression Standards
Fundamentals of Multimedia, Chapter 9 Chapter 9 Image Compression Standards 9.1 The JPEG Standard 9.2 The JPEG2000 Standard 9.3 The JPEG-LS Standard 9.4 Bi-level Image Compression Standards 9.5 Further Exploration 1 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 9.1 The JPEG Standard JPEG is an image compression standard that was developed • by the “Joint Photographic Experts Group”. JPEG was for- mally accepted as an international standard in 1992. JPEG is a lossy image compression method. It employs a • transform coding method using the DCT (Discrete Cosine Transform). An image is a function of i and j (or conventionally x and y) • in the spatial domain. The 2D DCT is used as one step in JPEG in order to yield a frequency response which is a function F (u, v) in the spatial frequency domain, indexed by two integers u and v. 2 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 Observations for JPEG Image Compression The effectiveness of the DCT transform coding method in • JPEG relies on 3 major observations: Observation 1: Useful image contents change relatively slowly across the image, i.e., it is unusual for intensity values to vary widely several times in a small area, for example, within an 8 8 × image block. much of the information in an image is repeated, hence “spa- • tial redundancy”. 3 Li & Drew c Prentice Hall 2003 ! Fundamentals of Multimedia, Chapter 9 Observations for JPEG Image Compression (cont’d) Observation 2: Psychophysical experiments suggest that hu- mans are much less likely to notice the loss of very high spatial frequency components than the loss of lower frequency compo- nents. -
(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. -
Task-Aware Quantization Network for JPEG Image Compression
Task-Aware Quantization Network for JPEG Image Compression Jinyoung Choi1 and Bohyung Han1 Dept. of ECE & ASRI, Seoul National University, Korea fjin0.choi,[email protected] Abstract. We propose to learn a deep neural network for JPEG im- age compression, which predicts image-specific optimized quantization tables fully compatible with the standard JPEG encoder and decoder. Moreover, our approach provides the capability to learn task-specific quantization tables in a principled way by adjusting the objective func- tion of the network. The main challenge to realize this idea is that there exist non-differentiable components in the encoder such as run-length encoding and Huffman coding and it is not straightforward to predict the probability distribution of the quantized image representations. We address these issues by learning a differentiable loss function that approx- imates bitrates using simple network blocks|two MLPs and an LSTM. We evaluate the proposed algorithm using multiple task-specific losses| two for semantic image understanding and another two for conventional image compression|and demonstrate the effectiveness of our approach to the individual tasks. Keywords: JPEG image compression, adaptive quantization, bitrate approximation. 1 Introduction Image compression is a classical task to reduce the file size of an input image while minimizing the loss of visual quality. This task has two categories|lossy and lossless compression. Lossless compression algorithms preserve the contents of input images perfectly even after compression, but their compression rates are typically low. On the other hand, lossy compression techniques allow the degra- dation of the original images by quantization and reduce the file size significantly compared to lossless counterparts. -
Towards the Second Edition of ISO 24707 Common Logic
Towards the Second Edition of ISO 24707 Common Logic Michael Gr¨uninger(with Tara Athan and Fabian Neuhaus) Miniseries on Ontologies, Rules, and Logic Programming for Reasoning and Applications January 9, 2014 Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 1 / 20 What Is Common Logic? Common Logic (published as \ISO/IEC 24707:2007 | Information technology Common Logic : a framework for a family of logic-based languages") is a language based on first-order logic, but extending it in several ways that ease the formulation of complex ontologies that are definable in first-order logic. Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 2 / 20 Semantics An interpretation I consists of a set URI , the universe of reference a set UDI , the universe of discourse, such that I UDI 6= ;; I UDI ⊆ URI ; a mapping intI : V ! URI ; ∗ a mapping relI from URI to subsets of UDI . Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 3 / 20 How Is Common Logic Being Used? Open Ontology Repositories COLORE (Common Logic Ontology Repository) colore.oor.net stl.mie.utoronto.ca/colore/ontologies.html OntoHub www.ontohub.org https://github.com/ontohub/ontohub Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014 4 / 20 How Is Common Logic Being Used? Ontology-based Standards Process Specification Language (ISO 18629) Date-Time Vocabulary (OMG) Foundational UML (OMG) Semantic Web Services Framework (W3C) OntoIOp (ISO WD 17347) Gr¨uninger ( Ontolog Forum) Common Logic (ISO 24707) January 9, 2014