Coding of Still Pictures
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ITU-T Rec. T.800 (08/2002) Information Technology
INTERNATIONAL TELECOMMUNICATION UNION ITU-T T.800 TELECOMMUNICATION (08/2002) STANDARDIZATION SECTOR OF ITU SERIES T: TERMINALS FOR TELEMATIC SERVICES Information technology – JPEG 2000 image coding system: Core coding system ITU-T Recommendation T.800 INTERNATIONAL STANDARD ISO/IEC 15444-1 ITU-T RECOMMENDATION T.800 Information technology – JPEG 2000 image coding system: Core coding system Summary This Recommendation | International Standard defines a set of lossless (bit-preserving) and lossy compression methods for coding bi-level, continuous-tone grey-scale, palletized color, or continuous-tone colour digital still images. This Recommendation | International Standard: – specifies decoding processes for converting compressed image data to reconstructed image data; – specifies a codestream syntax containing information for interpreting the compressed image data; – specifies a file format; – provides guidance on encoding processes for converting source image data to compressed image data; – provides guidance on how to implement these processes in practice. Source ITU-T Recommendation T.800 was prepared by ITU-T Study Group 16 (2001-2004) and approved on 29 August 2002. An identical text is also published as ISO/IEC 15444-1. ITU-T Rec. T.800 (08/2002 E) i FOREWORD The International Telecommunication Union (ITU) is the United Nations specialized agency in the field of telecommunications. The ITU Telecommunication Standardization Sector (ITU-T) is a permanent organ of ITU. ITU-T is responsible for studying technical, operating and tariff questions and issuing Recommendations on them with a view to standardizing telecommunications on a worldwide basis. The World Telecommunication Standardization Assembly (WTSA), which meets every four years, establishes the topics for study by the ITU-T study groups which, in turn, produce Recommendations on these topics. -
Free Lossless Image Format
FREE LOSSLESS IMAGE FORMAT Jon Sneyers and Pieter Wuille [email protected] [email protected] Cloudinary Blockstream ICIP 2016, September 26th DON’T WE HAVE ENOUGH IMAGE FORMATS ALREADY? • JPEG, PNG, GIF, WebP, JPEG 2000, JPEG XR, JPEG-LS, JBIG(2), APNG, MNG, BPG, TIFF, BMP, TGA, PCX, PBM/PGM/PPM, PAM, … • Obligatory XKCD comic: YES, BUT… • There are many kinds of images: photographs, medical images, diagrams, plots, maps, line art, paintings, comics, logos, game graphics, textures, rendered scenes, scanned documents, screenshots, … EVERYTHING SUCKS AT SOMETHING • None of the existing formats works well on all kinds of images. • JPEG / JP2 / JXR is great for photographs, but… • PNG / GIF is great for line art, but… • WebP: basically two totally different formats • Lossy WebP: somewhat better than (moz)JPEG • Lossless WebP: somewhat better than PNG • They are both .webp, but you still have to pick the format GOAL: ONE FORMAT THAT COMPRESSES ALL IMAGES WELL EXPERIMENTAL RESULTS Corpus Lossless formats JPEG* (bit depth) FLIF FLIF* WebP BPG PNG PNG* JP2* JXR JLS 100% 90% interlaced PNGs, we used OptiPNG [21]. For BPG we used [4] 8 1.002 1.000 1.234 1.318 1.480 2.108 1.253 1.676 1.242 1.054 0.302 the options -m 9 -e jctvc; for WebP we used -m 6 -q [4] 16 1.017 1.000 / / 1.414 1.502 1.012 2.011 1.111 / / 100. For the other formats we used default lossless options. [5] 8 1.032 1.000 1.099 1.163 1.429 1.664 1.097 1.248 1.500 1.017 0.302� [6] 8 1.003 1.000 1.040 1.081 1.282 1.441 1.074 1.168 1.225 0.980 0.263 Figure 4 shows the results; see [22] for more details. -
Quadtree Based JBIG Compression
Quadtree Based JBIG Compression B. Fowler R. Arps A. El Gamal D. Yang ISL, Stanford University, Stanford, CA 94305-4055 ffowler,arps,abbas,[email protected] Abstract A JBIG compliant, quadtree based, lossless image compression algorithm is describ ed. In terms of the numb er of arithmetic co ding op erations required to co de an image, this algorithm is signi cantly faster than previous JBIG algorithm variations. Based on this criterion, our algorithm achieves an average sp eed increase of more than 9 times with only a 5 decrease in compression when tested on the eight CCITT bi-level test images and compared against the basic non-progressive JBIG algorithm. The fastest JBIG variation that we know of, using \PRES" resolution reduction and progressive buildup, achieved an average sp eed increase of less than 6 times with a 7 decrease in compression, under the same conditions. 1 Intro duction In facsimile applications it is desirable to integrate a bilevel image sensor with loss- less compression on the same chip. Suchintegration would lower p ower consumption, improve reliability, and reduce system cost. To reap these b ene ts, however, the se- lection of the compression algorithm must takeinto consideration the implementation tradeo s intro duced byintegration. On the one hand, integration enhances the p os- sibility of parallelism which, if prop erly exploited, can sp eed up compression. On the other hand, the compression circuitry cannot b e to o complex b ecause of limitations on the available chip area. Moreover, most of the chip area on a bilevel image sensor must b e o ccupied by photo detectors, leaving only the edges for digital logic. -
JPEG and JPEG 2000
JPEG and JPEG 2000 Past, present, and future Richard Clark Elysium Ltd, Crowborough, UK [email protected] Planned presentation Brief introduction JPEG – 25 years of standards… Shortfalls and issues Why JPEG 2000? JPEG 2000 – imaging architecture JPEG 2000 – what it is (should be!) Current activities New and continuing work… +44 1892 667411 - [email protected] Introductions Richard Clark – Working in technical standardisation since early 70’s – Fax, email, character coding (8859-1 is basis of HTML), image coding, multimedia – Elysium, set up in ’91 as SME innovator on the Web – Currently looks after JPEG web site, historical archive, some PR, some standards as editor (extensions to JPEG, JPEG-LS, MIME type RFC and software reference for JPEG 2000), HD Photo in JPEG, and the UK MPEG and JPEG committees – Plus some work that is actually funded……. +44 1892 667411 - [email protected] Elysium in Europe ACTS project – SPEAR – advanced JPEG tools ESPRIT project – Eurostill – consensus building on JPEG 2000 IST – Migrator 2000 – tool migration and feature exploitation of JPEG 2000 – 2KAN – JPEG 2000 advanced networking Plus some other involvement through CEN in cultural heritage and medical imaging, Interreg and others +44 1892 667411 - [email protected] 25 years of standards JPEG – Joint Photographic Experts Group, joint venture between ISO and CCITT (now ITU-T) Evolved from photo-videotex, character coding First meeting March 83 – JPEG proper started in July 86. 42nd meeting in Lausanne, next week… Attendance through national -
MPEG Compression Is Based on Processing 8 X 8 Pixel Blocks
MPEG-1 & MPEG-2 Compression 6th of March, 2002, Mauri Kangas Contents: • MPEG Background • MPEG-1 Standard (shortly) • MPEG-2 Standard (shortly) • MPEG-1 and MPEG-2 Differencies • MPEG Encoding and Decoding • Colour sub-sampling • Motion Compensation • Slices, macroblocks, blocks, etc. • DCT • MPEG-2 bit stream syntax • MPEG-2 • Supported Features • Levels • Profiles • DVB Systems 1 © Mauri Kangas 2002 MPEG-1,2.PPT/ 24.02.2002 / Mauri Kangas MPEG Background • MPEG = Motion Picture Expert Group • ISO/IEC JTC1/SC29 • WG11 Motion Picture Experts Group (MPEG) • WG10 Joint Photographic Experts Group (JPEG) • WG7 Computer Graphics Experts Group (CGEG) • WG9 Joint Bi-level Image coding experts Group (JBIG) • WG12 Multimedia and Hypermedia information coding Experts Group (MHEG) • MPEG-1,2 Standardization 1988- • Requirement • System • Video • Audio • Implementation • Testing • Latest MPEG Standardization: MPEG-4, MPEG-7, MPEG-21 2 © Mauri Kangas 2002 MPEG-1,2.PPT/ 24.02.2002 / Mauri Kangas MPEG-1 Standard ISO/IEC 11172-2 (1991) "Coding of moving pictures and associated audio for digital storage media" Video • optimized for bitrates around 1.5 Mbit/s • originally optimized for SIF picture format, but not limited to it: • 352x240 pixels a 30 frames/sec [ NTSC based ] • 352x288 pixels at 25 frames/sec [ PAL based ] • progressive frames only - no direct provision for interlaced video applications, such as broadcast television Audio • joint stereo audio coding at 192 kbit/s (layer 2) System • mainly designed for error-free digital storage media • -
CISC 3635 [36] Multimedia Coding and Compression
Brooklyn College Department of Computer and Information Sciences CISC 3635 [36] Multimedia Coding and Compression 3 hours lecture; 3 credits Media types and their representation. Multimedia coding and compression. Lossless data compression. Lossy data compression. Compression standards: text, audio, image, fax, and video. Course Outline: 1. Media Types and Their Representations (Week 1-2) a. Text b. Analog and Digital Audio c. Digitization of Sound d. Musical Instrument Digital Interface (MIDI) e. Audio Coding: Pulse Code Modulation (PCM) f. Graphics and Images Data Representation g. Image File Formats: GIF, JPEG, PNG, TIFF, BMP, etc. h. Color Science and Models i. Fax j. Video Concepts k. Video Signals: Component, Composite, S-Video l. Analog and Digital Video: NTSC, PAL, SECAM, HDTV 2. Lossless Compression Algorithms (Week 3-4) a. Basics of Information Theory b. Run-Length Coding c. Variable Length Coding: Huffman Coding d. Basic Fax Compression e. Dictionary Based Coding: LZW Compression f. Arithmetic Coding g. Lossless Image Compression 3. Lossy Compression Algorithms (Week 5-6) a. Distortion Measures b. Quantization c. Transform Coding d. Wavelet Based Coding e. Embedded Zerotree of Wavelet (EZW) Coefficients 4. Basic Audio Compression (Week 7-8) a. Differential Coders: DPCM, ADPCM, DM b. Vocoders c. Linear Predictive Coding (LPC) d. CELP e. Audio Standards: G.711, G.726, G.723, G.728. G.729, etc. 5. Image Compression Standards (Week 9) a. The JPEG Standard b. JPEG 2000 c. JPEG-LS d. Bilevel Image Compression Standards: JBIG, JBIG2 6. Basic Video Compression (Week 10) a. Video Compression Based on Motion Compensation b. Search for Motion Vectors c. -
Analysis and Comparison of Compression Algorithm for Light Field Mask
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 12 (2017) pp. 3553-3556 © Research India Publications. http://www.ripublication.com Analysis and Comparison of Compression Algorithm for Light Field Mask Hyunji Cho1 and Hoon Yoo2* 1Department of Computer Science, SangMyung University, Korea. 2Associate Professor, Department of Media Software SangMyung University, Korea. *Corresponding author Abstract This paper describes comparison and analysis of state-of-the- art lossless image compression algorithms for light field mask data that are very useful in transmitting and refocusing the light field images. Recently, light field cameras have received wide attention in that they provide 3D information. Also, there has been a wide interest in studying the light field data compression due to a huge light field data. However, most of existing light field compression methods ignore the mask information which is one of important features of light field images. In this paper, we reports compression algorithms and further use this to achieve binary image compression by realizing analysis and comparison of the standard compression methods such as JBIG, JBIG 2 and PNG algorithm. The results seem to confirm that the PNG method for text data compression provides better results than the state-of-the-art methods of JBIG and JBIG2 for binary image compression. Keywords: Lossless compression, Image compression, Light Figure. 2. Basic architecture from raw images to RGB and filed compression, Plenoptic coding mask images INTRODUCTION The LF camera provides a raw image captured from photosensor with microlens, as depicted in Fig. 1. The raw Light field (LF) cameras, also referred to as plenoptic cameras, data consists of 10 bits per pixel precision in little-endian differ from regular cameras by providing 3D information of format. -
File Format Guidelines for Management and Long-Term Retention of Electronic Records
FILE FORMAT GUIDELINES FOR MANAGEMENT AND LONG-TERM RETENTION OF ELECTRONIC RECORDS 9/10/2012 State Archives of North Carolina File Format Guidelines for Management and Long-Term Retention of Electronic records Table of Contents 1. GUIDELINES AND RECOMMENDATIONS .................................................................................. 3 2. DESCRIPTION OF FORMATS RECOMMENDED FOR LONG-TERM RETENTION ......................... 7 2.1 Word Processing Documents ...................................................................................................................... 7 2.1.1 PDF/A-1a (.pdf) (ISO 19005-1 compliant PDF/A) ........................................................................ 7 2.1.2 OpenDocument Text (.odt) ................................................................................................................... 3 2.1.3 Special Note on Google Docs™ .......................................................................................................... 4 2.2 Plain Text Documents ................................................................................................................................... 5 2.2.1 Plain Text (.txt) US-ASCII or UTF-8 encoding ................................................................................... 6 2.2.2 Comma-separated file (.csv) US-ASCII or UTF-8 encoding ........................................................... 7 2.2.3 Tab-delimited file (.txt) US-ASCII or UTF-8 encoding .................................................................... 8 2.3 -
DICOM PS3.5 2021C
PS3.5 DICOM PS3.5 2021d - Data Structures and Encoding Page 2 PS3.5: DICOM PS3.5 2021d - Data Structures and Encoding Copyright © 2021 NEMA A DICOM® publication - Standard - DICOM PS3.5 2021d - Data Structures and Encoding Page 3 Table of Contents Notice and Disclaimer ........................................................................................................................................... 13 Foreword ............................................................................................................................................................ 15 1. Scope and Field of Application ............................................................................................................................. 17 2. Normative References ....................................................................................................................................... 19 3. Definitions ....................................................................................................................................................... 23 4. Symbols and Abbreviations ................................................................................................................................. 27 5. Conventions ..................................................................................................................................................... 29 6. Value Encoding ............................................................................................................................................... -
Lossless Image Compression
Lossless Image Compression C.M. Liu Perceptual Signal Processing Lab College of Computer Science National Chiao-Tung University http://www.csie.nctu.edu.tw/~cmliu/Courses/Compression/ Office: EC538 (03)5731877 [email protected] Lossless JPEG (1992) 2 ITU Recommendation T.81 (09/92) Compression based on 8 predictive modes ( “selection values): 0 P(x) = x (no prediction) 1 P(x) = W 2 P(x) = N 3 P(x) = NW NW N 4 P(x) = W + N - NW 5 P(x) = W + ⎣(N-NW)/2⎦ W x 6 P(x) = N + ⎣(W-NW)/2⎦ 7 P(x) = ⎣(W + N)/2⎦ Sequence is then entropy-coded (Huffman/AC) Lossless JPEG (2) 3 Value 0 used for differential coding only in hierarchical mode Values 1, 2, 3 One-dimensional predictors Values 4, 5, 6, 7 Two-dimensional Value 1 (W) Used in the first line of samples At the beginning of each restart Selected predictor used for the other lines Value 2 (N) Used at the start of each line, except first P-1 Default predictor value: 2 At the start of first line Beginning of each restart Lossless JPEG Performance 4 JPEG prediction mode comparisons JPEG vs. GIF vs. PNG Context-Adaptive Lossless Image Compression [Wu 95/96] 5 Two modes: gray-scale & bi-level We are skipping the lossy scheme for now Basic ideas find the best context from the info available to encoder/decoder estimate the presence/lack of horizontal/vertical features CALIC: Initial Prediction 6 if dh−dv > 80 // sharp horizontal edge X* = N else if dv−dh > 80 // sharp vertical edge X* = W else { // assume smoothness first X* = (N+W)/2 +(NE−NW)/4 if dh−dv > 32 // horizontal edge X* = (X*+N)/2 -
The Essentials of Video Transcoding Enabling Acquisition, Interoperability, and Distribution in Digital Media
Telestream Whitepaper The Essentials of Video Transcoding Enabling Acquisition, Interoperability, and Distribution in Digital Media The intent of this paper is to Contents provide an overview of the Introduction Page 1 transcoding basics you’ll need to Digital media formats Page 2 know in order to make informed The essence of compression Page 3 decisions about implementing The right tool for the job Page 4 transcoding in your workflow. That Bridging the gaps Page 5 involves explaining the elements of (media exchange between diverse systems) the various types of digital media Transcoding workflows Page 6 files used for different purposes, Conclusion Page 7 how transcoding works upon those elements, what challenges might arise in moving from one Introduction format to another, and what It’s been more than three decades now since digital media emerged from the workflows might be most effective lab into real-world applications, igniting a communications revolution that for transcoding in various common continues to play out today. First up was the Compact Disc, featuring situations. compression-free LPCM digital audio that was designed to fully reconstruct the source at playback. With digital video, on the other hand, it was clear from the outset that most recording, storage, and playback situations wouldn’t have the data capacity required to describe a series of images with complete accuracy. And so the race was on to develop compression/decompression algorithms (codecs) that could adequately capture a video source while requiring as little data bandwidth as possible. 1 Telestream Whitepaper The good news is that astonishing progress has been Transcoding bypasses this tedious, inefficient scenario. -
Conversion of MP3 to AAC in the Compressed Domain
Conversion of MP3 to AAC in the Compressed Domain Koichi Takagi Satoshi Miyaji Shigeyuki Sakazawa Yasuhiro Takishima KDDI R&D Labs. Inc. Fujimino-shi, Saitama 356-8502 Japan Abstract— In this paper, we propose an efficient algorithm transcoding, even in transcoding for bit rate conversion where the for conversion of an MP3 stream into AAC. Generally, this kind encoding method is not changed. of conversion, transcoding, requires full decoding and re- Therefore, in this paper, we propose a novel algorithm for encoding. However, the re-encoding based transcoding process conversion of MP3 to AAC in the compressed data domain with may result in degradation of quality and take longer than the least quality degradation. We analyzed the encoding parameters encoding from a PCM signal. This paper proposes a method to of MP3 and AAC, which consists of side information, scale factors, inherit the frame structure and quantization scale from MP3 to and MDCT coefficients, and then tried to figure out the differences AAC. This enables a reduction in the iteration process, which and similarities. Consequently, we realized faster conversion of requires the most time in the AAC encoding process, without MP3 to AAC compared with the simple transcoding technique. incurring degradation in quality. Experimental results show that the proposed method can perform bitstream domain transcoding This paper is organized as follows. Section II provides an at high speed while maintaining a high level of audio quality. overview of the related techniques and outlines the proposed method. In Section III, performance of the proposed algorithm is Keywords—MP3; AAC; Transcoding; Audio conversion; Scale compared with the existing transcoding technique.