Lossless and Nearly-Lossless Image Compression Based on Combinatorial Transforms
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High Efficiency, Moderate Complexity Video Codec Using Only RF IPR
Thor High Efficiency, Moderate Complexity Video Codec using only RF IPR draft-fuldseth-netvc-thor-00 Arild Fuldseth, Gisle Bjontegaard (Cisco) IETF 93 – Prague, CZ – July 2015 1 Design principles • Moderate complexity to allow real-time implementation in SW on common HW, as well as new HW designs • Basic building blocks from well-known hybrid approach (motion compensated prediction and transform coding) • Common design elements in modern codecs – Larger block sizes and transforms, up to 64x64 – Quarter pixel interpolation, motion vector prediction, etc. • Cisco RF IPR (note well: declaration filed on draft) – Deblocking, transforms, etc. (some also essential in H.265/4) • Avoid non-RF IPR – If/when others offer RF IPR, design/performance will improve 2 Encoder Architecture Input Transform Quantizer Entropy Output video Coding bitstream - Inverse Transform Intra Frame Prediction Loop filters Inter Frame Prediction Reconstructed Motion Frame Estimation Memory 3 Decoder Architecture Input Entropy Inverse Bitstream Decoding Transform Intra Frame Prediction Loop filters Inter Frame Prediction Output video Reconstructed Frame Memory 4 Block Structure • Super block (SB) 64x64 • Quad-tree split into coding blocks (CB) >= 8x8 • Multiple prediction blocks (PB) per CB • Intra: 1 PB per CB • Inter: 1, 2 (rectangular) or 4 (square) PBs per CB • 1 or 4 transform blocks (TB) per CB 5 Coding-block modes • Intra • Inter0 MV index, no residual information • Inter1 MV index, residual information • Inter2 Explicit motion vector information, residual information -
Hardware for Speech and Audio Coding
Linköping Studies in Science and Technology Thesis No. 1093 Hardware for Speech and Audio Coding Mikael Olausson LiU-TEK-LIC-2004:22 Department of Electrical Engineering Linköpings universitet, SE-581 83 Linköping, Sweden Linköping 2004 Linköping Studies in Science and Technology Thesis No. 1093 Hardware for Speech and Audio Coding Mikael Olausson LiU-TEK-LIC-2004:22 Department of Electrical Engineering Linköpings universitet, SE-581 83 Linköping, Sweden Linköping 2004 ISBN 91-7373-953-7 ISSN 0280-7971 ii Abstract While the Micro Processors (MPUs) as a general purpose CPU are converging (into Intel Pentium), the DSP processors are diverging. In 1995, approximately 50% of the DSP processors on the market were general purpose processors, but last year only 15% were general purpose DSP processors on the market. The reason general purpose DSP processors fall short to the application specific DSP processors is that most users want to achieve highest performance under mini- mized power consumption and minimized silicon costs. Therefore, a DSP proces- sor must be an Application Specific Instruction set Processor (ASIP) for a group of domain specific applications. An essential feature of the ASIP is its functional acceleration on instruction level, which gives the specific instruction set architecture for a group of appli- cations. Hardware acceleration for digital signal processing in DSP processors is essential to enhance the performance while keeping enough flexibility. In the last 20 years, researchers and DSP semiconductor companies have been working on different kinds of accelerations for digital signal processing. The trade-off be- tween the performance and the flexibility is always an interesting question because all DSP algorithms are "application specific"; the acceleration for audio may not be suitable for the acceleration of baseband signal processing. -
I Introduction
PPM Performance with BWT Complexity: A New Metho d for Lossless Data Compression Michelle E ros California Institute of Technology e [email protected] Abstract This work combines a new fast context-search algorithm with the lossless source co ding mo dels of PPM to achieve a lossless data compression algorithm with the linear context-search complexity and memory of BWT and Ziv-Lemp el co des and the compression p erformance of PPM-based algorithms. Both se- quential and nonsequential enco ding are considered. The prop osed algorithm yields an average rate of 2.27 bits per character bp c on the Calgary corpus, comparing favorably to the 2.33 and 2.34 bp c of PPM5 and PPM and the 2.43 bp c of BW94 but not matching the 2.12 bp c of PPMZ9, which, at the time of this publication, gives the greatest compression of all algorithms rep orted on the Calgary corpus results page. The prop osed algorithm gives an average rate of 2.14 bp c on the Canterbury corpus. The Canterbury corpus web page gives average rates of 1.99 bp c for PPMZ9, 2.11 bp c for PPM5, 2.15 bp c for PPM7, and 2.23 bp c for BZIP2 a BWT-based co de on the same data set. I Intro duction The Burrows Wheeler Transform BWT [1] is a reversible sequence transformation that is b ecoming increasingly p opular for lossless data compression. The BWT rear- ranges the symb ols of a data sequence in order to group together all symb ols that share the same unb ounded history or \context." Intuitively, this op eration is achieved by forming a table in which each row is a distinct cyclic shift of the original data string. -
Dc5m United States Software in English Created at 2016-12-25 16:00
Announcement DC5m United States software in english 1 articles, created at 2016-12-25 16:00 articles set mostly positive rate 10.0 1 3.8 Google’s Brotli Compression Algorithm Lands to Windows Edge Microsoft has announced that its Edge browser has started using Brotli, the compression algorithm that Google open-sourced last year. 2016-12-25 05:00 1KB www.infoq.com Articles DC5m United States software in english 1 articles, created at 2016-12-25 16:00 1 /1 3.8 Google’s Brotli Compression Algorithm Lands to Windows Edge Microsoft has announced that its Edge browser has started using Brotli, the compression algorithm that Google open-sourced last year. Brotli is on by default in the latest Edge build and can be previewed via the Windows Insider Program. It will reach stable status early next year, says Microsoft. Microsoft touts a 20% higher compression ratios over comparable compression algorithms, which would benefit page load times without impacting client-side CPU costs. According to Google, Brotli uses a whole new data format , which makes it incompatible with Deflate but ensures higher compression ratios. In particular, Google says, Brotli is roughly as fast as zlib when decompressing and provides a better compression ratio than LZMA and bzip2 on the Canterbury Corpus. Brotli appears to be especially tuned for the web , that is for offline encoding and online decoding of Web assets, or Android APKs. Google claims a compression ratio improvement of 20–26% over its own Zopfli algorithm, which still provides the best compression ratio of any deflate algorithm. -
Efficient Multi-Codec Support for OTT Services: HEVC/H.265 And/Or AV1?
Efficient Multi-Codec Support for OTT Services: HEVC/H.265 and/or AV1? Christian Timmerer†,‡, Martin Smole‡, and Christopher Mueller‡ ‡Bitmovin Inc., †Alpen-Adria-Universität San Francisco, CA, USA and Klagenfurt, Austria, EU ‡{firstname.lastname}@bitmovin.com, †{firstname.lastname}@itec.aau.at Abstract – The success of HTTP adaptive streaming is un- multiple versions (e.g., different resolutions and bitrates) and disputed and technical standards begin to converge to com- each version is divided into predefined pieces of a few sec- mon formats reducing market fragmentation. However, other onds (typically 2-10s). A client first receives a manifest de- obstacles appear in form of multiple video codecs to be sup- scribing the available content on a server, and then, the client ported in the future, which calls for an efficient multi-codec requests pieces based on its context (e.g., observed available support for over-the-top services. In this paper, we review the bandwidth, buffer status, decoding capabilities). Thus, it is state of the art of HTTP adaptive streaming formats with re- able to adapt the media presentation in a dynamic, adaptive spect to new services and video codecs from a deployment way. perspective. Our findings reveal that multi-codec support is The existing different formats use slightly different ter- inevitable for a successful deployment of today's and future minology. Adopting DASH terminology, the versions are re- services and applications. ferred to as representations and pieces are called segments, which we will use henceforth. The major differences between INTRODUCTION these formats are shown in Table 1. We note a strong differ- entiation in the manifest format and it is expected that both Today's over-the-top (OTT) services account for more than MPEG's media presentation description (MPD) and HLS's 70 percent of the internet traffic and this number is expected playlist (m3u8) will coexist at least for some time. -
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter
CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Optimized AV1 Inter Prediction using Binary classification techniques A graduate project submitted in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering by Alex Kit Romero May 2020 The graduate project of Alex Kit Romero is approved: ____________________________________ ____________ Dr. Katya Mkrtchyan Date ____________________________________ ____________ Dr. Kyle Dewey Date ____________________________________ ____________ Dr. John J. Noga, Chair Date California State University, Northridge ii Dedication This project is dedicated to all of the Computer Science professors that I have come in contact with other the years who have inspired and encouraged me to pursue a career in computer science. The words and wisdom of these professors are what pushed me to try harder and accomplish more than I ever thought possible. I would like to give a big thanks to the open source community and my fellow cohort of computer science co-workers for always being there with answers to my numerous questions and inquiries. Without their guidance and expertise, I could not have been successful. Lastly, I would like to thank my friends and family who have supported and uplifted me throughout the years. Thank you for believing in me and always telling me to never give up. iii Table of Contents Signature Page ................................................................................................................................ ii Dedication ..................................................................................................................................... -
Arxiv:2002.01657V1 [Eess.IV] 5 Feb 2020 Port Lossless Model to Compress Images Lossless
LEARNED LOSSLESS IMAGE COMPRESSION WITH A HYPERPRIOR AND DISCRETIZED GAUSSIAN MIXTURE LIKELIHOODS Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan. ABSTRACT effectively in [12, 13, 14]. Some methods decorrelate each Lossless image compression is an important task in the field channel of latent codes and apply deep residual learning to of multimedia communication. Traditional image codecs improve the performance as [15, 16, 17]. However, deep typically support lossless mode, such as WebP, JPEG2000, learning based lossless compression has rarely discussed. FLIF. Recently, deep learning based approaches have started One related work is L3C [18] to propose a hierarchical archi- to show the potential at this point. HyperPrior is an effective tecture with 3 scales to compress images lossless. technique proposed for lossy image compression. This paper In this paper, we propose a learned lossless image com- generalizes the hyperprior from lossy model to lossless com- pression using a hyperprior and discretized Gaussian mixture pression, and proposes a L2-norm term into the loss function likelihoods. Our contributions mainly consist of two aspects. to speed up training procedure. Besides, this paper also in- First, we generalize the hyperprior from lossy model to loss- vestigated different parameterized models for latent codes, less compression model, and propose a loss function with L2- and propose to use Gaussian mixture likelihoods to achieve norm for lossless compression to speed up training. Second, adaptive and flexible context models. Experimental results we investigate four parameterized distributions and propose validate our method can outperform existing deep learning to use Gaussian mixture likelihoods for the context model. -
Implementing Associative Coder of Buyanovsky (ACB)
Implementing Associative Coder of Buyanovsky (ACB) data compression by Sean Michael Lambert A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Montana State University © Copyright by Sean Michael Lambert (1999) Abstract: In 1994 George Mechislavovich Buyanovsky published a basic description of a new data compression algorithm he called the “Associative Coder of Buyanovsky,” or ACB. The archive program using this idea, which he released in 1996 and updated in 1997, is still one of the best general compression utilities available. Despite this, the ACB algorithm is still barely understood by data compression experts, primarily because Buyanovsky never published a detailed description of it. ACB is a new idea in data compression, merging concepts from existing statistical and dictionary-based algorithms with entirely original ideas. This document presents several variations of the ACB algorithm and the details required to implement a basic version of ACB. IMPLEMENTING ASSOCIATIVE CODER OF BUYANOVSKY (ACB) DATA COMPRESSION by Sean Michael Lambert A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science MONTANA STATE UNIVERSITY-BOZEMAN Bozeman, Montana April 1999 © COPYRIGHT by Sean Michael Lambert 1999 All Rights Reserved ii APPROVAL of a thesis submitted by Sean Michael Lambert This thesis has been read by each member of the thesis committee and has been found to be satisfactory regarding content, English usage, format, citations, bibliographic style, and consistency, and is ready for submission to the College of Graduate Studies. Brendan Mumey U /l! ^ (Signature) Date Approved for the Department of Computer Science J. -
An Analysis of XML Compression Efficiency
An Analysis of XML Compression Efficiency Christopher J. Augeri1 Barry E. Mullins1 Leemon C. Baird III Dursun A. Bulutoglu2 Rusty O. Baldwin1 1Department of Electrical and Computer Engineering Department of Computer Science 2Department of Mathematics and Statistics United States Air Force Academy (USAFA) Air Force Institute of Technology (AFIT) USAFA, Colorado Springs, CO Wright Patterson Air Force Base, Dayton, OH {chris.augeri, barry.mullins}@afit.edu [email protected] {dursun.bulutoglu, rusty.baldwin}@afit.edu ABSTRACT We expand previous XML compression studies [9, 26, 34, 47] by XML simplifies data exchange among heterogeneous computers, proposing the XML file corpus and a combined efficiency metric. but it is notoriously verbose and has spawned the development of The corpus was assembled using guidelines given by developers many XML-specific compressors and binary formats. We present of the Canterbury corpus [3], files often used to assess compressor an XML test corpus and a combined efficiency metric integrating performance. The efficiency metric combines execution speed compression ratio and execution speed. We use this corpus and and compression ratio, enabling simultaneous assessment of these linear regression to assess 14 general-purpose and XML-specific metrics, versus prioritizing one metric over the other. We analyze compressors relative to the proposed metric. We also identify key collected metrics using linear regression models (ANOVA) versus factors when selecting a compressor. Our results show XMill or a simple comparison of means, e.g., X is 20% better than Y. WBXML may be useful in some instances, but a general-purpose compressor is often the best choice. 2. XML OVERVIEW XML has gained much acceptance since first proposed in 1998 by Categories and Subject Descriptors the World-Wide Web Consortium (W3C). -
Course Outline & Schedule
Course Outline & Schedule Call US 408-759-5074 or UK +44 20 7620 0033 Open Media Encoding Techniques Course Code PWL396 Duration 3 Day Course Price $2,815 Course Description Video, TV and Image technology today dominates Internet Services. Whether it be for live TV, Streamed movies or video clips within social media video images are everywhere. The long-term efficiency of services depends upon the methods and mechanisms used to encode these services. We have relied upon the developments from the Digital Video Broadcast industry, ISO, MPEG and the ITU to provide us with standard ways to achieve this. However the patent royalty cost is now considered to be holding back this efficient development. All commercial users of the normal encoding used such as H.264, H.265, HEVC and other standardised codecs are required to pay royalties for using this technology through a firm of US patent Lawyers known as MPEG-LA. Each new ITU- T standard encoding requires new and increased payments. The Alliance for Open Media is founded by leading Internet companies focused on developing next-generation media formats, codecs and technologies in the public interest. The new Alliance is committing its collective technology and expertise to meet growing Internet demand for top-quality video, audio, imagery and streaming across devices of all kinds and for users worldwide. The aim is to develop royalty free standardized encoding based upon the technology contributed by its members. This course provides a technical study of Video Coding and the technologies which the developing Open Media implementations are based upon. -
Improving Compression-Ratio in Backup
Institutionen för systemteknik Department of Electrical Engineering Examensarbete Improving compression ratio in backup Examensarbete utfört i Informationskodning/Bildkodning av Mattias Zeidlitz Författare Mattias Zeidlitz LITH-ISY-EX--12/4588--SE Linköping 2012 TEKNISKA HÖGSKOLAN LINKÖPINGS UNIVERSITET Department of Electrical Engineering Linköpings tekniska högskola Linköping University Institutionen för systemteknik S-581 83 Linköping, Sweden 581 83 Linköping Improving compression-ratio in backup ............................................................................ Examensarbete utfört i Informationskodning/Bildkodning vid Linköpings tekniska högskola av Mattias Zeidlitz ............................................................. LITH-ISY-EX--12/4588--SE Presentationsdatum Institution och avdelning 2012-06-13 Institutionen för systemteknik Publiceringsdatum (elektronisk version) Department of Electrical Engineering Datum då du ämnar publicera exjobbet Språk Typ av publikation ISBN (licentiatavhandling) Svenska Licentiatavhandling ISRN LITH-ISY-EX--12/4588--SE x Annat (ange nedan) x Examensarbete Serietitel (licentiatavhandling) C-uppsats D-uppsats Engelska Rapport Serienummer/ISSN (licentiatavhandling) Antal sidor Annat (ange nedan) 58 URL för elektronisk version http://www.ep.liu.se Publikationens titel Improving compression ratio in backup Författare Mattias Zeidlitz Sammanfattning Denna rapport beskriver ett examensarbete genomfört på Degoo Backup AB i Stockholm under våren 2012. Syftet var att designa en kompressionssvit -
Benefits of Hardware Data Compression in Storage Networks Gerry Simmons, Comtech AHA Tony Summers, Comtech AHA SNIA Legal Notice
Benefits of Hardware Data Compression in Storage Networks Gerry Simmons, Comtech AHA Tony Summers, Comtech AHA SNIA Legal Notice The material contained in this tutorial is copyrighted by the SNIA. Member companies and individuals may use this material in presentations and literature under the following conditions: Any slide or slides used must be reproduced without modification The SNIA must be acknowledged as source of any material used in the body of any document containing material from these presentations. This presentation is a project of the SNIA Education Committee. Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 2 © 2007 Storage Networking Industry Association. All Rights Reserved. Abstract Benefits of Hardware Data Compression in Storage Networks This tutorial explains the benefits and algorithmic details of lossless data compression in Storage Networks, and focuses especially on data de-duplication. The material presents a brief history and background of Data Compression - a primer on the different data compression algorithms in use today. This primer includes performance data on the specific compression algorithms, as well as performance on different data types. Participants will come away with a good understanding of where to place compression in the data path, and the benefits to be gained by such placement. The tutorial will discuss technological advances in compression and how they affect system level solutions. Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 3 © 2007 Storage Networking Industry Association. All Rights Reserved. Agenda Introduction and Background Lossless Compression Algorithms System Implementations Technology Advances and Compression Hardware Power Conservation and Efficiency Conclusion Oct 17, 2007 Benefits of Hardware Data Compression in Storage Networks 4 © 2007 Storage Networking Industry Association.