DOCSLIB.ORG

Iso/Iec Fdis 18181-2 International

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Iso/Iec Fdis 18181-2 International FINAL INTERNATIONAL ISO/IEC DRAFT STANDARD FDIS 18181-2 ISO/IEC JTC 1/SC 29 Secretariat: JISC Information technology — JPEG XL Voting begins on: image coding system — 2021-07-01 Part 2: Voting terminates on: 2021-08-26 File format iTeh STANDARD PREVIEW (standards.iteh.ai) ISO/IEC FDIS 18181-2 https://standards.iteh.ai/catalog/standards/sist/c106fc96-4e43-4e72-845f- 5434f7e5b0d2/iso-iec-fdis-18181-2 RECIPIENTS OF THIS DRAFT ARE INVITED TO SUBMIT, WITH THEIR COMMENTS, NOTIFICATION OF ANY RELEVANT PATENT RIGHTS OF WHICH THEY ARE AWARE AND TO PROVIDE SUPPOR TING DOCUMENTATION. IN ADDITION TO THEIR EVALUATION AS Reference number BEING ACCEPTABLE FOR INDUSTRIAL, TECHNO- LOGICAL, COMMERCIAL AND USER PURPOSES, ISO/IEC FDIS 18181-2:2021(E) DRAFT INTERNATIONAL STANDARDS MAY ON OCCASION HAVE TO BE CONSIDERED IN THE LIGHT OF THEIR POTENTIAL TO BECOME STAN- DARDS TO WHICH REFERENCE MAY BE MADE IN NATIONAL REGULATIONS. © ISO/IEC 2021 ISO/IEC FDIS 18181-2:2021(E) iTeh STANDARD PREVIEW (standards.iteh.ai) ISO/IEC FDIS 18181-2 https://standards.iteh.ai/catalog/standards/sist/c106fc96-4e43-4e72-845f- 5434f7e5b0d2/iso-iec-fdis-18181-2 COPYRIGHT PROTECTED DOCUMENT © ISO/IEC 2021 All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address belowCP 401or ISO’s • Ch. member de Blandonnet body in 8 the country of the requester. ISOCH-1214 copyright Vernier, office Geneva Phone: +41 22 749 01 11 Website: www.iso.org PublishedEmail: [email protected] Switzerland ii © ISO/IEC 2021 – All rights reserved ISO/IEC FDIS 18181-2:2021(E) Contents Page Foreword ........................................................................................................................................................................................................................................iv 1 Scope ................................................................................................................................................................................................................................. 1 2 Normative references ...................................................................................................................................................................................... 1 3 Terms and definitions ..................................................................................................................................................................................... 1 4 General ............................................................................................................................................................................................................................ 2 5 File organization ................................................................................................................................................................................................... 2 6 Data types and numerical values ........................................................................................................................................................ 4 7 Graphical descriptions ................................................................................................................................................................................... 4 8 Binary format of a box .................................................................................................................................................................................... 5 9 Box types ....................................................................................................................................................................................................................... 6 ....................................................................................................................................................................... 6 .............................................................................................................................................................................................. 6 9.1 JPEG XL signature ....................................................................................................................................................................................................... box 6 9.2 File type box ................................................................................................................................................................................................... 6 9.3 Level box .......................................................................................................................................................................................................... 6 9.4 JUMBF box ......................................................................................................................................................................................................... 7 9.5 Exif box ..................................................................................................................................................................... 7 9.6 XML boxiTeh .....................................................................................................................................................................................STANDARD PREVIEW 7 9.7 Brotli-compressed box ................................................................................................................................................................. 9 9.8 Frame index box (standar ...............................................................................................................................................ds.iteh.ai) 9 9.9 JPEG XL codestream box ........................................................................................................................... 9 9.10 JPEG XL partial ........................................................................................................................................................................................codestreamISO /boxIEC FDIS 18181-2 9 9.11 JPEG9.11.2 hbitstreamt tpsReconstruction://standa rreconstructionds.iteh.ai/c aproceduretalog/st adatandar ....................................................................................................................................d boxs/sist/c106fc96-4e43-4e72-845f- 12 9.11.1 Syntax Bibliography .............................................................................................................................................................................................................................5434f7e5b0d2/iso-iec-fdis-18181-2 16 © ISO/IEC 2021 – All rights reserved iii ISO/IEC FDIS 18181-2:2021(E) Foreword ISO (the International Organization for Standardization) and IEC (the International Electrotechnical members of ISO or IEC participate in the development of International Standards through technical Commission) form the specialized system for worldwide standardization. National bodies that are committeesorganizations, established governmental by the and respective non-governmental, organization in liaison to deal with with ISO particularand IEC, also fields take of part technical in the activity.work. ISO and IEC technical committees collaborate in fields of mutual interest. Other international The procedures used to develop this document and those intended for its further maintenance are described in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for editorial rules of the ISO/IEC Directives, Part 2 (see www .iso .org/ directives or www .iec .ch/ members the different refdocs types). of document should be noted. This document was drafted in accordance with the _experts/ Attention is drawn to the possibility that some of the elements of this document may be the subject ofIntroduction patent rights. and/or ISO on and the ISOIEC listshall of patentnot be declarations held responsible received for (see identifying www .iso any .org/ or patents all such) or thepatent IEC rights.list of patent Details declarations of any patent received rights (see identified patents.iec.ch during). the development of the document will be in the constitute an endorsement. Any trade name used in this idocumentTeh S isT informationANDA RgivenD forP RtheE convenienceVIEW of users and does not (standards.iteh.ai) ForWorld an Trade explanation Organization of the (WTO) voluntary principles nature in theof standards, Technical Barriersthe meaning to Trade of (TBT)ISO specific see www terms .iso .org/and expressionsiso/ foreword related .html. In to the conformity IEC, see www assessment, .iec .ch/IS understandingO /asIEC well FDIS as 18 1information8 -standards1-2 .about ISO's adherence to the https://standards.iteh.ai/catalog/standards/sist/c106fc96-4e43-4e72-8Information45f- technology, Subcommittee SC 29, Coding of audio, picture,5434 fmultimedia7e5b0d2/iso-ie andc-fdi shypermedia-18181-2 information. This document was prepared by Joint Technical Committee ISO/IEC JTC 1, A list of all parts in the ISO/IEC 18181 series can be found on the ISO website. complete listing of these bodies can be found at www .iso .org/ members .html and www
Load more

Recommended publications
  • National Critical Information Infrastructure Protection Centre Common Vulnerabilities and Exposures(CVE) Report
    National Critical Information Infrastructure Protection Centre Common Vulnerabilities and Exposures(CVE) Report https://nciipc.gov.in 01 - 15 Mar 2021 Vol. 08 No. 05 Weakness Publish Date CVSS Description & CVE ID Patch NCIIPC ID Application Accellion fta Improper Accellion FTA 9_12_432 Neutralization and earlier is affected by of Special argument injection via a Elements in crafted POST request to an A-ACC-FTA- 02-Mar-21 7.5 N/A Output Used by admin endpoint. The fixed 160321/1 a Downstream version is FTA_9_12_444 Component and later. ('Injection') CVE ID : CVE-2021-27730 Improper Accellion FTA 9_12_432 Neutralization and earlier is affected by of Input During stored XSS via a crafted A-ACC-FTA- Web Page 02-Mar-21 4.3 POST request to a user N/A 160321/2 Generation endpoint. The fixed version ('Cross-site is FTA_9_12_444 and later. Scripting') CVE ID : CVE-2021-27731 adguard adguard_home An issue was discovered in AdGuard before 0.105.2. An Improper attacker able to get the https://githu Restriction of user's cookie is able to b.com/Adgua A-ADG- Excessive 03-Mar-21 5 bruteforce their password rdTeam/AdG ADGU- Authentication offline, because the hash of uardHome/is 160321/3 Attempts the password is stored in sues/2470 the cookie. CVE ID : CVE-2021-27935 Afterlogic webmail_pro Improper 04-Mar-21 6.8 An issue was discovered in https://auror A-AFT- CVSS Scoring Scale 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 Page 1 of 166 Weakness Publish Date CVSS Description & CVE ID Patch NCIIPC ID Limitation of a AfterLogic Aurora through amail.wordpr WEBM- Pathname to a 8.5.3 and WebMail Pro ess.com/202 160321/4 Restricted through 8.5.3, when DAV is 1/02/03/add Directory enabled.
    [Show full text]
  • An Improved Objective Metric to Predict Image Quality Using Deep Neural Networks
    https://doi.org/10.2352/ISSN.2470-1173.2019.12.HVEI-214 © 2019, Society for Imaging Science and Technology An Improved Objective Metric to Predict Image Quality using Deep Neural Networks Pinar Akyazi and Touradj Ebrahimi; Multimedia Signal Processing Group (MMSPG); Ecole Polytechnique Fed´ erale´ de Lausanne; CH 1015, Lausanne, Switzerland Abstract ages in a full reference (FR) framework, i.e. when the reference Objective quality assessment of compressed images is very image is available, is a difference-based metric called the peak useful in many applications. In this paper we present an objec- signal to noise ratio (PSNR). PSNR and its derivatives do not tive quality metric that is better tuned to evaluate the quality of consider models based on the human visual system (HVS) and images distorted by compression artifacts. A deep convolutional therefore often result in low correlations with subjective quality neural networks is used to extract features from a reference im- ratings. [1]. Metrics such as structural similarity index (SSIM) age and its distorted version. Selected features have both spatial [2], multi-scale structural similarity index (MS-SSIM) [3], feature and spectral characteristics providing substantial information on similarity index (FSIM) [4] and visual information fidelity (VIF) perceived quality. These features are extracted from numerous [5] use models motivated by HVS and natural scenes statistics, randomly selected patches from images and overall image qual- resulting in better correlations with viewers’ opinion. ity is computed as a weighted sum of patch scores, where weights Numerous machine learning based objective quality metrics are learned during training. The model parameters are initialized have been reported in the literature.
    [Show full text]
  • An Optimization of JPEG-LS Using an Efficient and Low-Complexity
    Received April 26th, 2021. Revised June 27th, 2021. Accepted July 23th, 2021. Digital Object Identifier 10.1109/ACCESS.2021.3100747 LOCO-ANS: An Optimization of JPEG-LS Using an Efficient and Low-Complexity Coder Based on ANS TOBÍAS ALONSO , GUSTAVO SUTTER , AND JORGE E. LÓPEZ DE VERGARA High Performance Computing and Networking Research Group, Escuela Politécnica Superior, Universidad Autónoma de Madrid, Spain. {tobias.alonso, gustavo.sutter, jorge.lopez_vergara}@uam.es This work was supported in part by the Spanish Research Agency under the project AgileMon (AEI PID2019-104451RB-C21). ABSTRACT Near-lossless compression is a generalization of lossless compression, where the codec user is able to set the maximum absolute difference (the error tolerance) between the values of an original pixel and the decoded one. This enables higher compression ratios, while still allowing the control of the bounds of the quantization errors in the space domain. This feature makes them attractive for applications where a high degree of certainty is required. The JPEG-LS lossless and near-lossless image compression standard combines a good compression ratio with a low computational complexity, which makes it very suitable for scenarios with strong restrictions, common in embedded systems. However, our analysis shows great coding efficiency improvement potential, especially for lower entropy distributions, more common in near-lossless. In this work, we propose enhancements to the JPEG-LS standard, aimed at improving its coding efficiency at a low computational overhead, particularly for hardware implementations. The main contribution is a low complexity and efficient coder, based on Tabled Asymmetric Numeral Systems (tANS), well suited for a wide range of entropy sources and with simple hardware implementation.
    [Show full text]
  • Benchmarking JPEG XL Image Compression
    Benchmarking JPEG XL image compression a a c a b Jyrki Alakuijala​ ,​ Sami Boukortt​ ,​ Touradj Ebrahimi​ ,​ Evgenii Kliuchnikov​ ,​ Jon Sneyers​ ,​ Evgeniy Upenikc,​ Lode Vandevennea,​ Luca Versaria,​ Jan Wassenberg1*a a ​ ​ b ​ ​ Google Switzerland, 8002 Zurich, Switzerland; ​ ​ Cloudinary, Santa Clara, CA 95051, USA; c ​ Multimedia Signal Processing Group, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. ABSTRACT JPEG XL is a practical, royalty-free codec for scalable web distribution and efficient compression of high-quality photographs. It also includes previews, progressiveness, animation, transparency, high dynamic range, wide color gamut, and high bit depth. Users expect faithful reproductions of ever higher-resolution images. Experiments performed during standardization have shown the feasibility of economical storage without perceptible quality loss, lossless transcoding of existing JPEG, and fast software encoders and decoders. We analyse the results of subjective and objective evaluations versus existing codecs including HEVC and JPEG. New image codecs have to co-exist with their previous generations for several years. JPEG XL is unique in providing value for both existing JPEGs as well as new users. It includes coding tools to reduce the transmission and storage costs of JPEG by 22% while allowing byte-for-byte exact reconstruction of the original JPEG. Avoiding transcoding and additional artifacts helps to preserve our digital heritage. Applications require fast and low-power decoding. JPEG XL was designed to benefit from multicore and SIMD, and actually decodes faster than JPEG. We report the resulting speeds on ARM and x86 CPUs. To enable reproduction of these results, we open sourced the JPEG XL software in 2019. Keywords:​ image compression, JPEG XL, perceptual visual quality, image fidelity 1.
    [Show full text]
  • Denoising-Based Image Compression for Connectomics
    bioRxiv preprint doi: https://doi.org/10.1101/2021.05.29.445828; this version posted May 30, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Denoising-based Image Compression for Connectomics David Minnen1,#, Michał Januszewski1,#, Alexander Shapson-Coe2, Richard L. Schalek2, Johannes Ballé1, Jeff W. Lichtman2, Viren Jain1 1 Google Research 2 Department of Molecular and Cellular Biology & Center for Brain Science, Harvard University # Equal contribution Abstract Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of reconstruction accuracy. Main Text Progress in the reconstruction and analysis of neural circuits has recently been accelerated by advances in 3d volume electron microscopy (EM) and automated methods for neuron segmentation and annotation1,2. In particular, contemporary serial section EM approaches are capable of imaging hundreds of megapixels per second3,4, which has led to the successful acquisition of petabyte-scale volumes of human5 and mouse6 cortex. However, scaling further poses substantial challenges to computational infrastructure; nanometer resolution imaging of a whole mouse brain7 could, for example, produce one million terabytes (an exabyte) of data.
    [Show full text]
  • Comprehensive Assessment of Image Compression Algorithms
    Comprehensive Assessment of Image Compression Algorithms Michela Testolina, Evgeniy Upenik, and Touradj Ebrahimi Multimedia Signal Processing Group (MMSPG) Ecole Polytechnique F´ed´eralede Lausanne (EPFL) CH-1015 Lausanne, Switzerland Email: firstname.lastname@epfl.ch ABSTRACT JPEG image coding standard has been a dominant format in a wide range of applications in soon three decades since it has been released as an international standard. The landscape of applications and services based on pictures has evolved since the original JPEG image coding algorithm was designed. As a result of progress in the state of the art image coding, several alternative image coding approaches have been proposed to replace the legacy JPEG with mixed results. The majority among them have not been able to displace the unique position that the legacy JPEG currently occupies as the ubiquitous image coding format of choice. Those that have been successful, have been so in specific niche applications. In this paper, we analyze why all attempts to replace JPEG have been limited so far, and discuss additional features other than compression efficiency that need to be present in any modern image coding algorithm to increase its chances of success. Doing so, we compare rate distortion performance of state of the art conventional and learning based image coding, in addition to other desired features in advanced multimedia applications such as transcoding. We conclude the paper by highlighting the remaining challenges that need to be overcome in order to design a successful future generation image coding algorithm. Keywords: Image compression, lossy compression, deep learning, perceptual visual quality, JPEG standard 1.
    [Show full text]
  • Ukládání a Komprese Obrazu
    Úvod Reprezentace barvy Kódování a komprese dat Formáty pro ukládání obrazu Komprese videa Literatura Ukládání a komprese obrazu Pavel Strachota FJFI CVUTˇ v Praze 5. dubna 2021 Úvod Reprezentace barvy Kódování a komprese dat Formáty pro ukládání obrazu Komprese videa Literatura Obsah 1 Úvod 2 Reprezentace barvy 3 Kódování a komprese dat 4 Formáty pro ukládání obrazu 5 Komprese videa Úvod Reprezentace barvy Kódování a komprese dat Formáty pro ukládání obrazu Komprese videa Literatura Obsah 1 Úvod 2 Reprezentace barvy 3 Kódování a komprese dat 4 Formáty pro ukládání obrazu 5 Komprese videa Úvod Reprezentace barvy Kódování a komprese dat Formáty pro ukládání obrazu Komprese videa Literatura Reprezentace obrazu vektorová - popis geometrických objekt˚ua jejich vlastností (polohy, barvy, prekrýváníˇ atd.) nezávisí na rozlišení zobrazovacího zarízeníˇ lze libovnolneˇ škálovat priˇ zachování kvality rastrová - popis obrazu pomocí matice pixel˚u konecnéˇ rozlišení vysoká pamet’ovᡠnárocnostˇ =) snaha o kompresi informace Úvod Reprezentace barvy Kódování a komprese dat Formáty pro ukládání obrazu Komprese videa Literatura Obsah 1 Úvod 2 Reprezentace barvy 3 Kódování a komprese dat 4 Formáty pro ukládání obrazu 5 Komprese videa Úvod Reprezentace barvy Kódování a komprese dat Formáty pro ukládání obrazu Komprese videa Literatura Reprezentace barvy cernobílýˇ obraz - 1 bit na pixel šedotónový obraz - 8 bit˚una pixel, 256 stupn˚ušediˇ RGB reprezentace 5+6+5 bit˚u(565 High Color mode) 8+8+8 bit˚uTrue Color mode reprezentace v jiném barevném prostoru
    [Show full text]
  • Coding of Still Pictures
    ISO/IEC JTC 1/SC29/WG1N77011 77th Meeting, Macau, China, October 23-27, 2017 ISO/IEC JTC 1/SC 29/WG 1 (ITU-T SG16) Coding of Still Pictures JBIG JPEG Joint Bi-level Image Joint Photographic Experts Group Experts Group TITLE: Draft Call for Proposals for a Next-Generation Image Coding Standard (JPEG XL) SOURCE: WG1 PROJECT: TBD STATUS: Draft REQUESTED ACTION: Provide feedback and response DISTRIBUTION: Public Contact: ISO/IEC JTC 1/SC 29/WG 1 Convener – Prof. Touradj Ebrahimi EPFL/STI/IEL/GR-EB, Station 11, CH-1015 Lausanne, Switzerland Tel: +41 21 693 2606, Fax: +41 21 693 7600, E-mail: [email protected] - 1 - ISO/IEC JTC 1/SC29/WG1N77011 77th Meeting, Macau, China, October 23-27, 2017 Draft Call for Proposals on Next-Generation Image Coding (JPEG XL) Summary The JPEG Committee has launched the Next-Generation Image Coding activity, also referred to as JPEG XL. This activity aims to develop a standard for image coding that offers substantially better compression efficiency than existing image formats (e.g. >60% over JPEG-1), along with features desirable for web distribution and efficient compression of high-quality images. The JPEG Committee intends to publish a final Call for Proposals (CfP) following its 78th meeting (January 2018), with the objective of seeking technologies that fulfil the objectives and scope of the Next- Generation Image Coding activity. This document is a draft of the CfP, and is offered for a public review period ending 19 January 2018. Comments are welcome and should be submitted to the contacts listed in Section 10.
    [Show full text]
  • A Standard Framework For
    Vrije Universiteit Brussel JPEG Pleno: a standard framework for representing and signaling plenoptic modalities Schelkens, Peter; Alpaslan, Zahir Y.; Ebrahimi, Touradj; Oh, Kwan-Jung; Pereira, Fernando ; Pinheiro, Antonio; Tabus, Ioan; Chen, Zhibo Published in: Proceedings of SPIE Optics + Photonics: Applications of Digital Image Processing XLI DOI: 10.1117/12.2323404 Publication date: 2018 License: CC BY Document Version: Final published version Link to publication Citation for published version (APA): Schelkens, P., Alpaslan, Z. Y., Ebrahimi, T., Oh, K-J., Pereira, F., Pinheiro, A., ... Chen, Z. (2018). JPEG Pleno: a standard framework for representing and signaling plenoptic modalities. In A. G. Tescher (Ed.), Proceedings of SPIE Optics + Photonics: Applications of Digital Image Processing XLI (Vol. 10752, pp. 107521P). [107521P] (APPLICATIONS OF DIGITAL IMAGE PROCESSING XLI). SPIE. https://doi.org/10.1117/12.2323404 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
    [Show full text]
  • Standards Action
    PUBLISHED WEEKLY BY THE AMERICAN NATIONAL STANDARDS INSTITUTE 25 WEST 43RD STREET NY, NY 10036 VOL. 52| NO. 33 August 13, 2021 CONTENTS American National Standards Project Initiation Notification System (PINS) ............................................................... 2 Call for Comment on Standards Proposals .................................................................. 5 Final Actions - (Approved ANS) .................................................................................. 17 Call for Members (ANS Consensus Bodies) ................................................................ 25 Accreditation Announcements (Standards Developers) ............................................ 27 Meeting Notices (Standards Developers) .................................................................. 28 American National Standards (ANS) Process ............................................................. 29 ANS Under Continuous Maintenance ........................................................................ 30 ANSI-Accredited Standards Developer Contact Information ..................................... 31 International Standards ISO and IEC Draft Standards ....................................................................................... 34 ISO Newly Published Standards ................................................................................. 39 International Organization for Standardization (ISO) ................................................ 41 Registration of Organization Names in the United States .........................................
    [Show full text]
  • Evaluating Image Compression Methods on Two Dimensional Height Representations
    Linköping University | Department of Electrical Engineering Master’s thesis, 30 ECTS | Information Coding 2020 | LIU-ISY/ISY-A-EX-A--20/5344--SE Evaluating Image Compression Methods on Two Dimensional Height Representations Oscar Sjöberg Supervisor : Harald Nautsch Examiner : Ingemar Ragnemalm External supervisor : Filip Thorardsson Linköpings universitet SE–581 83 Linköping +46 13 28 10 00 , www.liu.se Upphovsrätt Detta dokument hålls tillgängligt på Internet - eller dess framtida ersättare - under 25 år från publicer- ingsdatum under förutsättning att inga extraordinära omständigheter uppstår. Tillgång till dokumentet innebär tillstånd för var och en att läsa, ladda ner, skriva ut enstaka ko- pior för enskilt bruk och att använda det oförändrat för ickekommersiell forskning och för undervis- ning. Överföring av upphovsrätten vid en senare tidpunkt kan inte upphäva detta tillstånd. All annan användning av dokumentet kräver upphovsmannens medgivande. För att garantera äktheten, säker- heten och tillgängligheten finns lösningar av teknisk och administrativ art. Upphovsmannens ideella rätt innefattar rätt att bli nämnd som upphovsman i den omfattning som god sed kräver vid användning av dokumentet på ovan beskrivna sätt samt skydd mot att dokumentet ändras eller presenteras i sådan form eller i sådant sammanhang som är kränkande för upphovsman- nens litterära eller konstnärliga anseende eller egenart. För ytterligare information om Linköping University Electronic Press se förlagets hemsida http://www.ep.liu.se/. Copyright The publishers will keep this document online on the Internet - or its possible replacement - for a period of 25 years starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to down- load, or to print out single copies for his/hers own use and to use it unchanged for non-commercial research and educational purpose.
    [Show full text]
  • Coding of Still Pictures
    ISO/IEC JTC 1/SC29/WG1 N83058 83rd JPEG Meeting, Geneva, Switzerland, 17-22 March 2019 ISO/IEC JTC 1/SC 29/WG 1 (ITU-T SG16) Coding of Still Pictures JBIG JPEG Joint Bi-level Image Joint Photographic Experts Group Experts Group TITLE: Report on the State-of-the-art of Learning based Image Coding SOURCE: Requirements EDITORS: João Ascenso, Pinar Akayzi STATUS: Final REQUESTED ACTION: Distribute DISTRIBUTION: Public Contact: ISO/IEC JTC 1/SC 29/WG 1 Convener – Prof. Touradj Ebrahimi EPFL/STI/IEL/GR-EB, Station 11, CH-1015 Lausanne, Switzerland Tel: +41 21 693 2606, Fax: +41 21 693 7600, E-mail: [email protected] Page 1 1. Purpose of This Document In the last few years, several learning-based image coding solutions have been proposed to take advantage of recent advances in this field. The driving force was the development of improved neural network models for several image processing areas, such as denoising and super-resolution, which have taken advantage of the availability of large image datasets and special hardware, such as the highly parallelizable graphic processing units (GPUs). This document marks the beginning of the work to be conducted in JPEG and has the following objectives: • Present a list of learning-based image coding solutions from the literature in an organized way. • Present a list of software implementations of deep learning-based image codecs public available. • Present a list of data sets that may be used for exploration studies. Despite the advances, there are several challenges in this type of coding solutions, namely which encoder/decoder architectures are more promising (e.g.
    [Show full text]