DICOM Compression 2002
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Binary Image Compression Using Neighborhood Coding
BINARY IMAGE COMPRESSION USING NEIGHBORHOOD CODING Tiago B. A. de Carvalho1, Tsang Ing Ren1, George D.C. Cavalcanti1, Tsang Ing Jyh2 1Center of Informatics, Federal University of Pernambuco, Brazil. 2Alcatel-Lucent, Bell Labs, Belgium E-mail: 1{tbac,tir,gdcc}@cin.ufpe.br, [email protected] ABSTRACT Bitmap image format requires a reasonable great amount of computer memory, since for each pixel it is necessary a Compression plays an important role in the storage and set of bits to represent it, and a relatively small image can transmission of digital image. Binary image compression is contain millions of pixels. Even a binary image that uses just of essential value for document imaging. Here, we propose a a bit per pixel can demand a large amount of disk space. novel compression technique based on the concept of There are dozens of bitmap image formats that use neighborhood coding, which codes each pixel of an image compression techniques, e.g., TIFF, GIF, PNG, JBIG and according to the number of neighbor pixels in different JPEG. The TIFF format can also use different types of directions. The proposed technique is a lossless compression compression methods such as CCITT Group 3 or Group 4. scheme, which also uses run-length encoding (RLE) and We propose a novel lossless binary image compression Huffman coding. We evaluated and compared this method to technique based on neighborhood coding scheme described several image file format, using test images taken from the in [2]. The codification starts by transforming each pixel of MPEG-7 core experiment CE-shape and the binary image the image into a vector. -
Genetic Databases
Stefano Lonardi March, 2000 Compression of Biological Sequences by Greedy Off-line Textual Substitution Alberto Apostolico Stefano Lonardi Purdue University Università di Padova Genetic Databases § Massive § Growing exponentially Example: GenBank contains approximately 4,654,000,000 bases in 5,355,000 sequence records as of December 1999 Data Compression Conference 2000 1 Stefano Lonardi March, 2000 DNA Sequence Records Composed by annotations (in English) and DNA bases (on the alphabet {A,C,G,T,U,M,R,W,S,Y,K,V,H,D,B,X,N}) >RTS2 RTS2 upstream sequence, from -200 to -1 TCTGTTATAGTACATATTATAGTACACCAATGTAAATCTGGTCCGGGTTACACAACACTT TGTCCTGTACTTTGAAAACTGGAAAAACTCCGCTAGTTGAAATTAATATCAAATGGAAAA GTCAGTATCATCATTCTTTTCTTGACAAGTCCTAAAAAGAGCGAAAACACAGGGTTGTTT GATTGTAGAAAATCACAGCG >MEK1 MEK1 upstream sequence, from -200 to -1 TTCCAATCATAAAGCATACCGTGGTYATTTAGCCGGGGAAAAGAAGAATGATGGCGGCTA AATTTCGGCGGCTATTTCATTCATTCAAGTATAAAAGGGAGAGGTTTGACTAATTTTTTA CTTGAGCTCCTTCTGGAGTGCTCTTGTACGTTTCAAATTTTATTAAGGACCAAATATACA ACAGAAAGAAGAAGAGCGGA >NDJ1 NDJ1 upstream sequence, from -200 to -1 ATAAAATCACTAAGACTAGCAACCACGTTTTGTTTTGTAGTTGAGAGTAATAGTTACAAA TGGAAGATATATATCCGTTTCGTACTCAGTGACGTACCGGGCGTAGAAGTTGGGCGGCTA TTTGACAGATATATCAAAAATATTGTCATGAACTATACCATATACAACTTAGGATAAAA ATACAGGTAGAAAAACTATA Problem Textual compression of DNA data is difficult, i.e., “standard” methods do not seem to exploit the redundancies (if any) inherent to DNA sequences cfr. C.Nevill-Manning, I.H.Witten, “Protein is incompressible”, DCC99 Data Compression Conference 2000 2 Stefano Lonardi March, 2000 -
ARC-LEAP User Instructions for The
ARC-LEAP User Instructions Appalachian Regional Commission Local Economic Assessment Package prepared for the Appalachian Regional Commission prepared by Economic Development Research Group, Inc. Glen Weisbrod Teresa Lynch Margaret Collins January, 2004 ARC-LEAP User Instructions Appalachian Regional Commission Local Economic Assessment Package prepared for the Appalachian Regional Commission prepared by Economic Development Research Group, Inc. 2 Oliver Street, 9th Floor, Boston, MA 02109 Telephone 617.338.6775 Fax 617.338.1174 e-mail [email protected] Website www.edrgroup.com January, 2004 ARC-LEAP User Instructions PREFACE LEAP is a software tool that was designed and developed by Economic Development Research Group, Inc. (www.edrgroup.com) to assist practitioners in evaluating local economic development needs and opportunities. ARC-LEAP is a version of this tool developed specifically for the Appalachian Regional Commission (ARC) and it’s Local Development Districts (LDDs). Development of this user guide was funded by ARC as a companion to the ARC-LEAP analysis system. This document presents user instructions and technical documentation for ARC-LEAP. It is organized into three parts: I. overview of the ARC-LEAP tool II. instructions for users to obtain input information and run the analysis model III. interpretation of output tables . A separate Handbook document provides more detailed discussion of the economic development assessment process, including analysis of local economic performance, diagnosis of local strengths and weaknesses, and application of business opportunity information for developing an economic development strategy. Economic Development Research Group i ARC-LEAP User Instructions I. OVERVIEW The ARC-LEAP model serves to three related purposes, each aimed at helping practitioners identify target industries for economic development. -
Sequence Alignment/Map Format Specification
Sequence Alignment/Map Format Specification The SAM/BAM Format Specification Working Group 3 Jun 2021 The master version of this document can be found at https://github.com/samtools/hts-specs. This printing is version 53752fa from that repository, last modified on the date shown above. 1 The SAM Format Specification SAM stands for Sequence Alignment/Map format. It is a TAB-delimited text format consisting of a header section, which is optional, and an alignment section. If present, the header must be prior to the alignments. Header lines start with `@', while alignment lines do not. Each alignment line has 11 mandatory fields for essential alignment information such as mapping position, and variable number of optional fields for flexible or aligner specific information. This specification is for version 1.6 of the SAM and BAM formats. Each SAM and BAMfilemay optionally specify the version being used via the @HD VN tag. For full version history see Appendix B. Unless explicitly specified elsewhere, all fields are encoded using 7-bit US-ASCII 1 in using the POSIX / C locale. Regular expressions listed use the POSIX / IEEE Std 1003.1 extended syntax. 1.1 An example Suppose we have the following alignment with bases in lowercase clipped from the alignment. Read r001/1 and r001/2 constitute a read pair; r003 is a chimeric read; r004 represents a split alignment. Coor 12345678901234 5678901234567890123456789012345 ref AGCATGTTAGATAA**GATAGCTGTGCTAGTAGGCAGTCAGCGCCAT +r001/1 TTAGATAAAGGATA*CTG +r002 aaaAGATAA*GGATA +r003 gcctaAGCTAA +r004 ATAGCT..............TCAGC -r003 ttagctTAGGC -r001/2 CAGCGGCAT The corresponding SAM format is:2 1Charset ANSI X3.4-1968 as defined in RFC1345. -
PDF Image JBIG2 Compression and Decompression with JBIG2 Encoding and Decoding SDK Library | 1
PDF image JBIG2 compression and decompression with JBIG2 encoding and decoding SDK library | 1 JBIG2 is an image compression standard for bi-level images developed by the Joint bi-level Image Expert Group. It is suitable for lossless compression and lossy compression. According to the group’s press release, in its lossless mode, JBIG2 usually generates files that are one- third to one-fifth the size of the fax group 4 and twice the size of JBIG, which was previously released by the group. The double-layer compression standard. JBIG2 was released as an international standard ITU in 2000. JBIG2 compression JBIG2 is an international standard for bi-level image compression. By segmenting the image into overlapping and/or non-overlapping areas of text, halftones and general content, compression techniques optimized for each content type are used: *Text area: The text area is composed of characters that are well suited for symbol-based encoding methods. Usually, each symbol will correspond to a character bitmap, and a sub-image represents a character or text. For each uppercase and lowercase character used on the front face, there is usually only one character bitmap (or sub-image) in the symbol dictionary. For example, the dictionary will have an “a” bitmap, an “A” bitmap, a “b” bitmap, and so on. VeryUtils.com PDF image JBIG2 compression and decompression with JBIG2 encoding and decoding SDK library | 1 PDF image JBIG2 compression and decompression with JBIG2 encoding and decoding SDK library | 2 *Halftone area: Halftone areas are similar to text areas because they consist of patterns arranged in a regular grid. -
Official Service Contractor for The2018 International Bluegrass Music Association
International Bluegrass Musical Association OFFICIAL SERVICE September 26 - 29, 2018 CONTRACTOR Raleigh Convention Center Hall C Raleigh, NC Information and Order Forms Table of Contents General Information General Information................................................................2, 3 Payment Policy & Credit Card Authorization.........................4 Third Party Billing & Credit Card Charge Authorization..5,6 Color Chart for Drape, Table Skirts and Carpet...................7 Decorating Services Custom Booth Packages...........................................................8 Furnishing Rentals.......................................................................9 Mailing Address: Custom Signs and Graphics...................................................10 P. O. Box 49837 Greensboro, NC 27419 Labor Cleaning Service.........................................................................11 Street Address: Installation and Dismantle Labor............................................12 121 North Chimney Rock Road Exhibitor Appointed Contractor.........................................13,14 Greensboro, NC 27409 Material Handling Phone: (336) 315-5225 Material Handling General Information............................15,16 Fax: (336) 315-5220 Material Handling Rate Schedule and Order Form.......17,18 Shipping Labels........................................................................19 2 General 2 Information HOLLINS Exposition Services is pleased to have been selected as the Official Service Contractor for the2018 International -
Galileo Formats
Galileo Formats October 1998 edition Chapters INDEX Introduction Booking File Air Transportation Fares Cars Hotels LeisureShopper Document Production Queues Client File/TravelScreen Travel Information Miscellaneous SECURITY Sign On H/SON SON/Z217 or Sign on at own office SON/ followed by Z and a 1 to 3 character I.D.; the I.D. can be SON/ZHA initials, a number or a combination of both SON/ZGL4HA Sign on at branch agency SON/ followed by Z, own pseudo city code and a 1 to 3 character I.D. SON/Z7XX1/UMP Sign on at 4 character PCC branch agency SON/ followed by Z, own pseudo city code, second delimiter and 1 to 3 character I.D. SB Change to work area B SA/TA Change to work area A; different duty code TA (Training) SAI/ZHA Sign back into all work areas at own office SAI/ZGL4HA Sign back into all work areas at branch agency; SAI/ followed by Z, own pseudo city code and a 1 to 3 character I.D. Sign Off SAO Temporary sign out; incomplete Booking Files must be ignored or completed SOF Sign off; incomplete Booking Files must be ignored or completed SOF/ZHA Sign off override (at own office); incomplete transactions are not protected SOF/ZGL4HA Sign off override (at branch agency); incomplete transactions are not protected; SOF/ followed by Z, own pseudo city code and a 1 to 3 character I.D. SECURITY Security Profile STD/ZHA Display security profile, for sign on HA; once displayed, password may be changed SDA List security profiles created by user (second level authoriser and above) SDA/ZXXØ List security profiles associated with agency XXØ (second level authoriser and above) STD/ZXX1UMP or Display profile STD/ followed by Z, own pseudo city code, second delimiter if pseudo STD/Z7XX1/UMP city code is 4 characters and 1 to 3 character I.D. -
Arc Welding Solutions from ESAB
Arc Welding solutions from ESAB ESAB Welding & Cutting Products / esabna.com / 1.800.ESAB.123 A full line of arc welding equipment for every application, industry and environment. Arc Welding Equipment Table of Contents Description Page Description Page Process Description .....................................................2 Genuine Heliarc® Tig Torches Arc Welding Equipment Selection Guide ...................4 Heliarc Tig Torch Selection Guide ................................56 Compacts (power source with built-in wire feeder) Gas-Cooled Torches Caddy Mig C200i ............................................................6 HW-24 ...................................................................57 Migmaster™ 215 Pro/280 Pro .........................................8 HW-90 ...................................................................59 MIG, DC Power Sources, CV/CVCC HW-9 .....................................................................61 Aristo™ Mig U5000i .......................................................10 HW-17 ...................................................................63 Origo™ Mig 320/410 ......................................................12 HW-26 ...................................................................65 Origo Mig 4002c/6502c ................................................21 Water-Cooled Torches Wire Feeders - Semi-Automatic HW-20 ...................................................................67 MobileFeed™ 300AVS .....................................................15 HW-18 ...................................................................69 -
On the Spectral Theorem of Langlands
On the Spectral Theorem of Langlands Patrick Delorme April 22, 2021 A` Chantal Abstract We show that the Hilbert subspace of L2pGpF qzGpAqq generated by wave packets of Eisenstein series built from discrete series is the whole space. To- gether with the work of Lapid [18], it achieves a proof of the spectral theorem of R.P. Langlands based on the work of J. Bernstein and E. Lapid [7] on the meromorphic continuation of Eisenstein series from I use truncation on compact sets as J. Arthur did for the local trace formula in [2]. 1 Introduction We denote by E the isometry introduced by E. Lapid in [18], Theorem 2, whose proof involves the meromorphic continuation of of Eisenstein series built from discrete data. A dense subspace of its image is generated by wave packets of these Eisenstein series. We show that this image is equal to L2pGpF qzGpAqq. This is what Lapid calls the second half of the proof of the spectral theorem of Langlands. It achieves a new proof of the spectral theorem of R.P. Langlands ([17], [20]). Notice that the proof of Langlands describes the spectrum as residues of Eisenstein series built from cuspidal data. arXiv:2006.12893v3 [math.RT] 21 Apr 2021 One uses the notion of temperedness of automorphic forms introduced by J. Franke [15](cf. also [21] section 4.4). We show this notion of temperedness is weaker than the notion of temperedness introduced by Joseph Bernstein in [6]. We prove that for bounded sets of unitary parameters, the Eisenstein series are uniformly tempered when the parameter is unitary and bounded. -
Snowbound Supported File Formats
Snowbound Supported File Formats This document describes the file type number, descriptions, and read/write capabilities of all supported file formats. We have provided two tables of information, one sorted by file format name, and the other by the file type number. RasterMaster and VirtualViewer® HTML5 are powerful conversion tools that can transform your documents and images into many different formats. Some format types are limited in the amount of color (bit-depth) they support in an image. Some file formats read and write only black and white (1-bit deep) and other file formats support only color images (8+ bits deep). For many of these cases, the product automatically converts the pixel depth to the appropriate value, based on the output format specified. The chart below will help you determine whether your black and white or color document will be able to convert straight to the desired output format with no additional processing. When saving to a format, if the error returned is PIXEL_DEPTH_UNSUPPORTED (-21), the output format does not support the current bits per pixel of the image you are trying to save. The chart below will help you identify formats with compatible bit depths. 1 FILE FORMAT KEY File Format Description 1-bit Black and white or monochrome images. 4-bit, 8-bit, 16-bit Grayscale images, that may appear to be black and white, but contain much more information and are much larger than 1-bit. 8-bit, 16-bit, 24-bit, 32-bit Full color images. Please note that the higher the bit depth (bits per pixel), then the larger the size of the image on the disk or in memory. -
Still Image File Format Comparison
Still Image File Format Comparison Document I. Narrative Introduction Version of July 30, 2013 For review by the FADGI Still Image Working Group Background. The two FADGI Working Groups are exploring file formats for still images and video. The explorations are using similar, matrix-based tools to make comparisons relevant to preservation planning. The matrixes compare a limited number of formats in terms of roughly forty factors, grouped under the following general headings: Sustainability Factors Cost Factors System Implementation Factors (Full Lifecycle) Settings and Capabilities (Quality and Functionality Factors) The still image effort is led by the Government Printing Office and it is comparing formats suitable for reformatting (digitization). The formats being compared include JPEG 2000, JPEG (DCT), TIFF, PNG, and PDF, and several subtypes. The findings from this project will be integrated into the Working Group's continuing refinement of its general guideline for raster imaging. Document I - Narrative Introduction - Table of Contents Page 2 Introduction: File Format Sub-Group Page 2 Guiding Principles and Selection of File Formats Page 3 Sub-Group Deliverables: Summary Table and Detailed Matrix Page 4 Findings and Next Steps Other documents in the set Document II. Summary Table Document III. Detailed Matrix 1 FADGI Still Image Group: File Format Sub-Group Narrative Summary Introduction: File Format Sub-Group Since its inception, the FADGI Still Image Working Group’s work has mainly focused on guidelines related to image quality (e.g., resolution, sharpening, color encoding). As a supplement to these guidelines, the need has been identified to develop a set of recommendations for file encoding standards for archival and derivative renditions of digitized content, as the selection of format directly affects an implementer’s options in terms of compression, color encoding, and metadata support. -
TTHRESH: Tensor Compression for Multidimensional Visual Data
TTHRESH: Tensor Compression for Multidimensional Visual Data Rafael Ballester-Ripoll, Member, IEEE, Peter Lindstrom, Senior Member, IEEE, and Renato Pajarola, Senior Member, IEEE (a) Original (512MB) (b) 10:1 compression (51.2MB) (c) 300:1 compression (1.71MB) Fig. 1. (a) a 5123 isotropic turbulence volume [1]; (b) visually identical compression result; (c) result after extreme compression. Abstract—Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. We introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set. Index Terms—Transform-based compression, scientific visualization, higher-order singular value decomposition, Tucker model, tensor decompositions 1 INTRODUCTION Most scientific and visual computing applications face heavy compu- support for arbitrary dimensionality, ease of parallelization, topological tational and data management challenges when handling large and/or robustness, etc.