DOCSLIB.ORG

ERDAS ECW JPEG2000 Software Development Kit (SDK) Version 4.1 August 2010 Copyright © 2010 ERDAS, Inc

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
ERDAS ECW JPEG2000 Software Development Kit (SDK) Version 4.1 August 2010 Copyright © 2010 ERDAS, Inc ERDAS ECW JPEG2000 Software Development Kit (SDK) Version 4.1 August 2010 Copyright © 2010 ERDAS, Inc. All rights reserved. Printed in the United States of America. The information contained in this document is the exclusive property of ERDAS, Inc. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by ERDAS, Inc. All requests should be sent to the attention of: Manager, Technical Documentation ERDAS, Inc. 5051 Peachtree Corners Circle Suite 100 Norcross, GA 30092-2500 USA. The information contained in this document is subject to change without notice. Government Reserved Rights. MrSID technology incorporated in the Software was developed in part through a project at the Los Alamos National Laboratory, funded by the U.S. Government, managed under contract by the University of California (University), and is under exclusive commercial license to LizardTech, Inc. It is used under license from LizardTech. MrSID is protected by U.S. Patent No. 5,710,835. Foreign patents pending. The U.S. Government and the University have reserved rights in MrSID technology, including without limitation: (a) The U.S. Government has a non-exclusive, nontransferable, irrevocable, paid-up license to practice or have practiced throughout the world, for or on behalf of the United States, inventions covered by U.S. Patent No. 5,710,835 and has other rights under 35 U.S.C. § 200-212 and applicable implementing regulations; (b) If LizardTech's rights in the MrSID Technology terminate during the term of this Agreement, you may continue to use the Software. Any provisions of this license which could reasonably be deemed to do so would then protect the University and/or the U.S. Government; and (c) The University has no obligation to furnish any know-how, technical assistance, or technical data to users of MrSID software and makes no warranty or representation as to the validity of U.S. Patent 5,710,835 nor that the MrSID Software will not infringe any patent or other proprietary right. For further information about these provisions, contact LizardTech, 1008 Western Ave., Suite 200, Seattle, WA 98104. ERDAS, ERDAS IMAGINE, IMAGINE OrthoBASE, Stereo Analyst and IMAGINE VirtualGIS are registered trademarks; IMAGINE OrthoBASE Pro is a trademark of ERDAS, Inc. SOCET SET is a registered trademark of BAE Systems Mission Solutions. Other companies and products mentioned herein are trademarks or registered trademarks of their respective owners. ii iii Version History Version Date Comments Release 4.1 30 July 2010 Added opacity channel to ECW & JPEG 2000, GML georeference support for JPEG 2000 and quicker decoding for ECW and JPEG 2000. Release 3.3 19 June 2006 Enhancements, threading fixes, various bug-fixes, autoconf build support and licensing changes. Release 3.2 n/a Release 3.1 22 April 2005 Enhancements and maintenance Release 3.0 September 2004 JPEG 2000 Added Release 2.47 15 September 2003 Windows CE support Release 2.46 25 March 2002 Maintenance release Release 2.45 16 November 2001 GDT database support Release 2.4 9 July 2001 Map server integration Release 2.31 18 May 2001 Maintenance release Release 2.3 10 May 2001 Maintenance release Release 2.2 22 September 2000 NCSRenderer added Release 2.1 22 May 2000 Second release iv Version History Table of Contents Version History . iv Table of Contents . v Introduction . 1 Viewing and Printing this Document . .1 On-line Viewing . 1 Looking Up Topics . 2 Code listings . 3 Intended Audience . .3 What’s New in Version 4.1 . .3 What’s New in Version 3.3 . .4 What’s New in Version 3.0 . .4 What’s New in Version 2.0 . .4 Contacting Us . .4 Acknowledgements . .5 About Image Compression . 7 Introduction . .7 Lossless or Lossy Compression . .7 Wavelet Based Encoding . .8 ECW Compression . .9 Using Opacity Channels . 10 JPEG 2000 Compression . .10 Support for the NITF Standard . 12 Licensing and Installation . 13 System Requirements . .13 Operating system . 13 Platforms . 13 Third Party Packages . 13 Applications . 13 Licensing . .13 ECW JPEG2000 SDK Free Version . 13 ECW JPEG2000 SDK Licensed Editions . 14 Licensing the ECW JPEG2000 SDK . 14 Installation . .14 Directory Structure and Files. .17 Subdirectories and Files . 18 Development . 21 Introduction . .21 PC Library and Include Files . .21 Table of Contents v Project Settings - Visual C++ . .21 How Imagery is Accessed . .22 How to Read a View . 22 The SetFileView Concept . 22 Viewing Areas Smaller than your Application Window Area . 24 Requesting Odd-Aspect Views . 24 Selecting Bands from an Image File to View . 24 Blocking Reads Versus The Refresh Callback Interface . 25 Canceling Reads . .28 Multiple Image Views and Unlimited Image Size . .28 Error Handling . .28 Memory Management . .29 Memory Usage . 29 Caching . 29 ECWP Persistent Local Cache . 30 J2I Index Files . .30 Coordinate Information . .30 Transparent Proxying . .31 Delivering Your Application . .32 Creating Compressed Images . .32 Preserving Image Quality . 32 Optimizing the Compression Ratio . 33 Compressing Previously Compressed Images . 34 Compressing Hyperspectral Imagery . 34 Image Size Limitations . 34 Compression Directory Limitations . 35 Examples . 37 Compression Examples . .37 Compression Example 1 . 37 Compression Example 2 . 40 Compression Example 3 . 43 Decompression Examples . .45 Decompression Example 1 . 45 Decompression Example 2 . 46 Decompression Example 4 . 49 Decompression Example 5 . 51 Decompression Example 6 . 53 API Reference . 55 Introduction . .55 C API: Decompression Functions . .55 NCScbmCloseFileView . 55 NCScbmCloseFileViewEx . 56 NCScbmGetViewFileInfo . 56 NCScbmGetViewFileInfoEx . 57 NCScbmGetViewInfo . 57 NCScbmOpenFileView . 58 vi Table of Contents NCScbmReadViewLineBGR . 59 NCScbmReadViewLineBGRA . 59 NCScbmReadViewLineBIL . 60 NCScbmReadViewLineBILEx . 61 NCScbmReadViewLineRGB . 62 NCScbmReadViewLineRGBA . 62 NCScbmSetFileView . 63 NCScbmSetFileViewEx . 64 NCSecwSetConfig . 66 NCSecwSetIOCallbacks . 67 Decompression: Related Data Structures . .68 NCSFileViewFileInfo . 68 NCSFileViewFileInfoEx . 70 NCSFileBandInfo . 71 NCSFileViewSetInfo . 71 C API: Compression Functions . .73 NCSEcwCompressAllocClient . 73 NCSEcwCompressOpen . 74 NCSEcwCompress . 74 NCSEcwCompressClose . 75 NCSEcwCompressFreeClient . 75 NCSEcwCompressSetOEMKey . 76 Compression: Developer Defined Functions . .76 pReadCallback . 77 pStatusCallback . 77 pCancelCallback . 78 Compression: Related Data Structures. ..
Load more

Recommended publications
  • Lossy Image Compression Based on Prediction Error and Vector Quantisation Mohamed Uvaze Ahamed Ayoobkhan* , Eswaran Chikkannan and Kannan Ramakrishnan
    Ayoobkhan et al. EURASIP Journal on Image and Video Processing (2017) 2017:35 EURASIP Journal on Image DOI 10.1186/s13640-017-0184-3 and Video Processing RESEARCH Open Access Lossy image compression based on prediction error and vector quantisation Mohamed Uvaze Ahamed Ayoobkhan* , Eswaran Chikkannan and Kannan Ramakrishnan Abstract Lossy image compression has been gaining importance in recent years due to the enormous increase in the volume of image data employed for Internet and other applications. In a lossy compression, it is essential to ensure that the compression process does not affect the quality of the image adversely. The performance of a lossy compression algorithm is evaluated based on two conflicting parameters, namely, compression ratio and image quality which is usually measured by PSNR values. In this paper, a new lossy compression method denoted as PE-VQ method is proposed which employs prediction error and vector quantization (VQ) concepts. An optimum codebook is generated by using a combination of two algorithms, namely, artificial bee colony and genetic algorithms. The performance of the proposed PE-VQ method is evaluated in terms of compression ratio (CR) and PSNR values using three different types of databases, namely, CLEF med 2009, Corel 1 k and standard images (Lena, Barbara etc.). Experiments are conducted for different codebook sizes and for different CR values. The results show that for a given CR, the proposed PE-VQ technique yields higher PSNR value compared to the existing algorithms. It is also shown that higher PSNR values can be obtained by applying VQ on prediction errors rather than on the original image pixels.
    [Show full text]
  • Top 10 Reasons to Choose Autocad Raster Design 2010
    Top 10 Reasons to Choose AutoCAD Raster Design 2010 The Power of AutoCAD Minimize Costly Redrafting and Data Entry Time 1 Convert your scanned paper drawings to vector with Raster Design interactive and semiautomatic conversion tools. Use dynamic dimensioning and grip editing with vectorization Extend the power of AutoCAD® and tools to speed up the conversion and verification of raster AutoCAD-based software with AutoCAD® primitives such as lines, arcs, and circles. Easily convert Raster Design software. Make the most of continuous raster entities into AutoCAD polylines and 3D rasterized scanned drawings, maps, aerial polylines with vectorization following tools. Create and photos, satellite imagery, and digital elevation effectively manage hybrid drawings by converting only the models. Get more out of your raster data and necessary raster geometry, thereby speeding document enhance your designs, plans, presentations, and drawing revisions and updates. In addition, use optical and maps. AutoCAD Raster Design enables character recognition (OCR) functionality to recognize you to work in an AutoCAD environment, machine- and hand-printed text and tables on raster significantly reducing the need to purchase images to create AutoCAD text or multiline text (mtext). and learn multiple applications. RESULT: Speed project completion by unlocking and making the most of existing scanned engineering drawings, plans, Now Is the Time and maps. Use the Imagery You Require Take a look at how AutoCAD Raster Design AutoCAD Raster Design software supports a wide variety can help you improve your design process. 2 of industry-standard file formats, including single-image and multispectral file formats such as CALS, ER Mapper For more information about ECW, GIF, JPEG, JPEG 2000, LizardTech™ MrSID, TGA, AutoCAD Raster Design, go to TIFF, and more.
    [Show full text]
  • Need to Clip an Image?
    Hands On Need to Clip an Image? ArcGIS supplies several methods of clipping image data. In addition to using the Clip tool in the Raster toolbox in ArcToolbox, there are Use the Get Data other ways to quickly output a clipped raster. For ArcPad wizard Here are two simple interactive methods—using in the ArcPad the ArcPad toolbar that comes with ArcMap and toolbar in ArcMap exporting an image with the spatial extent of the to export an image current view that has the information available to clipped to the georeference it. current view or a specified spatial ArcPad Extraction Option extent. Use the ArcPad toolbar in ArcMap to extract an image. Clicking on the Get Data For ArcPad button in the toolbar will invoke a wizard for extracting data from the current map document and placing it in a checkout folder. This wizard writes vector features to new shapefiles and compresses raster images and saves them in MrSID format. The Get Data For ArcPad wizard contains several panels for defining properties related to the data being extracted. The layers 4. In the Export Map dialog box, choose a place The TIFF file format can also store in the map that will be extracted are specified to save the exported file and type a File name for georeferencing information internally. These files in the first pane. For this use of the extraction the export file. are called GeoTIFFs. ArcGIS can write GeoTIFF utility, check only the image layer to be clipped. 5. From the Save as Type drop-down box, tags for images exported from the Data View.
    [Show full text]
  • Understanding Compression of Geospatial Raster Imagery
    Understanding Compression of Geospatial Raster Imagery Document Overview This document was created for the North Carolina Geographic Information and Coordinating Council (GICC), http://ncgicc.com, by the GIS Technical Advisory Committee (TAC). Its purpose is to serve as a best practice or guidance document for GIS professionals that are compressing raster images. This document only addresses compressing geospatial raster data and specifically aerial or orthorectified imagery. It does not address compressing LiDAR data. Compression Overview Compression is the process of making data more compact so it occupies less disk storage space. The primary benefit of compressing raster data is reduction in file size. An added benefit is greatly improved performance over a network, because the user is transferring less data from a server to an application; however, compressed data must be decompressed to display in GIS software. The result may be slower raster display in GIS software than data that is not compressed. Compressed data can also increase CPU requirements on the server or desktop. Glossary of Common Terms Raster is a spatial data model made of rows and columns of cells. Each cell contains an attribute value identifying its color and location coordinate. Geospatial raster data like satellite images and aerial photographs are typically larger on average than vector data (predominately points, lines, or polygons). Compression is the process of making a (raster) file smaller while preserving all or most of the data it contains. Imagery compression enables storage of more data (image files) on a disk than if they were uncompressed. Compression ratio is the amount or degree of reduction of an image's file size.
    [Show full text]
  • Image Formats
    Image Formats Ioannis Rekleitis Many different file formats • JPEG/JFIF • Exif • JPEG 2000 • BMP • GIF • WebP • PNG • HDR raster formats • TIFF • HEIF • PPM, PGM, PBM, • BAT and PNM • BPG CSCE 590: Introduction to Image Processing https://en.wikipedia.org/wiki/Image_file_formats 2 Many different file formats • JPEG/JFIF (Joint Photographic Experts Group) is a lossy compression method; JPEG- compressed images are usually stored in the JFIF (JPEG File Interchange Format) >ile format. The JPEG/JFIF >ilename extension is JPG or JPEG. Nearly every digital camera can save images in the JPEG/JFIF format, which supports eight-bit grayscale images and 24-bit color images (eight bits each for red, green, and blue). JPEG applies lossy compression to images, which can result in a signi>icant reduction of the >ile size. Applications can determine the degree of compression to apply, and the amount of compression affects the visual quality of the result. When not too great, the compression does not noticeably affect or detract from the image's quality, but JPEG iles suffer generational degradation when repeatedly edited and saved. (JPEG also provides lossless image storage, but the lossless version is not widely supported.) • JPEG 2000 is a compression standard enabling both lossless and lossy storage. The compression methods used are different from the ones in standard JFIF/JPEG; they improve quality and compression ratios, but also require more computational power to process. JPEG 2000 also adds features that are missing in JPEG. It is not nearly as common as JPEG, but it is used currently in professional movie editing and distribution (some digital cinemas, for example, use JPEG 2000 for individual movie frames).
    [Show full text]
  • Parallel Heterogeneous Computing a Case Study on Accelerating JPEG2000 Coder
    Parallel Heterogeneous Computing A Case Study On Accelerating JPEG2000 Coder by Ro-To Le M.Sc., Brown University; Providence, RI, USA, 2009 B.Sc., Hanoi University of Technology; Hanoi, Vietnam, 2007 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The School of Engineering at Brown University PROVIDENCE, RHODE ISLAND May 2013 c Copyright 2013 by Ro-To Le This dissertation by Ro-To Le is accepted in its present form by The School of Engineering as satisfying the dissertation requirement for the degree of Doctor of Philosophy. Date R. Iris Bahar, Ph.D., Advisor Date Joseph L. Mundy, Ph.D., Advisor Recommended to the Graduate Council Date Vishal Jain, Ph.D., Reader Approved by the Graduate Council Date Peter M. Weber, Dean of the Graduate School iii Vitae Roto Le was born in Duc-Tho, Ha-Tinh, a countryside area in the Midland of Vietnam. He received his B.Sc., with Excellent Classification, in Electronics and Telecommunications from Hanoi University of Technology in 2007. Soon after re- ceiving his B.Sc., Roto came to Brown University to start a Ph.D. program in Com- puter Engineering in Fall 2007. His Ph.D. program was sponsored by a fellowship from the Vietnam Education Foundation, which was selectively nominated by the National Academies’ scientists. During his Ph.D. program, he earned a M.Sc. degree in Computer Engineering in 2009. Roto has been studying several aspects of modern computing systems, from hardware architecture and VLSI system design to high-performance software design. He has published several articles in designing a parallel JPEG2000 coder based on heterogeneous CPU-GPGPU systems and designing novel Three-Dimensional (3D) FPGA architectures.
    [Show full text]
  • Optimization of Image Compression for Scanned Document's Storage
    ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print) IJCST VOL . 4, Iss UE 1, JAN - MAR C H 2013 Optimization of Image Compression for Scanned Document’s Storage and Transfer Madhu Ronda S Soft Engineer, K L University, Green fields, Vaddeswaram, Guntur, AP, India Abstract coding, and lossy compression for bitonal (monochrome) images. Today highly efficient file storage rests on quality controlled This allows for high-quality, readable images to be stored in a scanner image data technology. Equivalently necessary and faster minimum of space, so that they can be made available on the web. file transfers depend on greater lossy but higher compression DjVu pages are highly compressed bitmaps (raster images)[20]. technology. A turn around in time is necessitated by reconsidering DjVu has been promoted as an alternative to PDF, promising parameters of data storage and transfer regarding both quality smaller files than PDF for most scanned documents. The DjVu and quantity of corresponding image data used. This situation is developers report that color magazine pages compress to 40–70 actuated by improvised and patented advanced image compression kB, black and white technical papers compress to 15–40 kB, and algorithms that have potential to regulate commercial market. ancient manuscripts compress to around 100 kB; a satisfactory At the same time free and rapid distribution of widely available JPEG image typically requires 500 kB. Like PDF, DjVu can image data on internet made much of operating software reach contain an OCR text layer, making it easy to perform copy and open source platform. In summary, application compression of paste and text search operations.
    [Show full text]
  • Forcepoint DLP Supported File Formats and Size Limits
    Forcepoint DLP Supported File Formats and Size Limits Supported File Formats and Size Limits | Forcepoint DLP | v8.8.1 This article provides a list of the file formats that can be analyzed by Forcepoint DLP, file formats from which content and meta data can be extracted, and the file size limits for network, endpoint, and discovery functions. See: ● Supported File Formats ● File Size Limits © 2021 Forcepoint LLC Supported File Formats Supported File Formats and Size Limits | Forcepoint DLP | v8.8.1 The following tables lists the file formats supported by Forcepoint DLP. File formats are in alphabetical order by format group. ● Archive For mats, page 3 ● Backup Formats, page 7 ● Business Intelligence (BI) and Analysis Formats, page 8 ● Computer-Aided Design Formats, page 9 ● Cryptography Formats, page 12 ● Database Formats, page 14 ● Desktop publishing formats, page 16 ● eBook/Audio book formats, page 17 ● Executable formats, page 18 ● Font formats, page 20 ● Graphics formats - general, page 21 ● Graphics formats - vector graphics, page 26 ● Library formats, page 29 ● Log formats, page 30 ● Mail formats, page 31 ● Multimedia formats, page 32 ● Object formats, page 37 ● Presentation formats, page 38 ● Project management formats, page 40 ● Spreadsheet formats, page 41 ● Text and markup formats, page 43 ● Word processing formats, page 45 ● Miscellaneous formats, page 53 Supported file formats are added and updated frequently. Key to support tables Symbol Description Y The format is supported N The format is not supported P Partial metadata
    [Show full text]
  • Terraexplorer® Pro Datasheet
    TerraExplorer® Pro Version 6.6.1 Datasheet www.SkylineGlobe.com TABLE OF CONTENTS OVERVIEW ............................................................................................................................................................. 4 PRODUCT MAIN FEATURES .................................................................................................................................... 4 LAYERS ................................................................................................................................................................... 5 FEATURE LAYERS ............................................................................................................................................................ 5 COMPLEX LAYERS ........................................................................................................................................................... 7 IMAGERY LAYERS ............................................................................................................................................................ 7 ELEVATION LAYERS ......................................................................................................................................................... 9 3D MESH LAYERS (3DML) .............................................................................................................................................. 9 OBJECTS ..............................................................................................................................................................
    [Show full text]
  • Image Compression and Its Effect on Data Khaled S
    Marshall University Marshall Digital Scholar Theses, Dissertations and Capstones 1-1-2004 Image Compression and its Effect on Data Khaled S. Alkharabsheh Follow this and additional works at: http://mds.marshall.edu/etd Part of the Geophysics and Seismology Commons, Graphics and Human Computer Interfaces Commons, and the Physical and Environmental Geography Commons Recommended Citation Alkharabsheh, Khaled S., "Image Compression and its Effect on Data" (2004). Theses, Dissertations and Capstones. Paper 441. This Thesis is brought to you for free and open access by Marshall Digital Scholar. It has been accepted for inclusion in Theses, Dissertations and Capstones by an authorized administrator of Marshall Digital Scholar. For more information, please contact [email protected]. IMAGE COMPRESSION AND ITS EFFECT ON DATA Thesis submitted to The Graduate College of Marshall University In partial fulfillment of the Requirements for the degree of Master of Science Physical Science GeoBioPhysical Modeling by Khaled S. Alkharabsheh Prof. Ralph Oberly, Committee Chairperson, Ph.D Prof. James Brumfield, Ph.D Marshall University Aug. 02, 2004 ii ABSTRACT IMAGE COMPRESSION AND ITS EFFECT ON IMAGES By Khaled S. Alkharabsheh This thesis is intended to define and study different image compression techniques, software programs, image formats (from early ones such as “GIF” to most recent ones such as “JPEG 2000”), compression effect on compressed data (compressed images), and its effectiveness and usefulness in reducing the file size and its transmission time, as a result. In many GeoBioPhysical applications, some information inside any image may be the keys to solve different kinds of problems and classify features. This kind of data and information has to be handled with care; i.e.
    [Show full text]
  • Compression of Aerial Images for Reduced-Color Devices
    Compression of aerial images for reduced-color devices Pasi Fränti and Ville Hautamäki Department of Computer Science, University of Joensuu Box 111, FIN-80101 Joensuu, FINLAND ABSTRACT Mobile devices are more often capable for locating the user on the globe by using GPS or network-based systems. The location is given to the user in a meaningful context such as map or aerial image. We study compression methods for reducing the amount of data required by aerial images. We consider the two well known lossless graphics file formats GIF and PNG, the compression standards JPEG and JPEG2000, a fractal compressor known as FIASCO, and commercial wavelet-based method developed for aerial images. It is also expected that the devices support fewer than 256 gray levels. It is therefore possible to get further reduction by quantizing the images prior to compression, for example, to two-bit per pixel, and then apply lossless compression methods such as JBIG, GIF and PNG. For four color mobile displays error diffusion dithered images approximate the original 8-bit color images quite well. The trade-off in dithering is that the lossless compression ratios decrease. Solution to this might be to store the 8-bit images compressed by using a decent lossy compressor such as JPEG2000. Quantization and dithering should happen only at the moment when the image is displayed. Keywords: Aerial images, image compression, mobile imaging, JBIG, JPEG, JPEG2000. 1. INTRODUCTION Aerial images are photographs of terrain taken from air or from satellite. Conventional maps can be represented in digital form as vector graphics or as bi-level bitmap.
    [Show full text]
  • Python Image Processing Using GDAL
    Python Image Processing using GDAL Trent Hare ([email protected]), Jay Laura, and Moses Milazzo Sept. 25, 2015 Why Python • Clean and simple • Expressive language • Dynamically typed • Automatic memory management • Interpreted Advantages • Minimizes time to develop, debug and maintain • Encourages good programming practices: • Modular and object-oriented programming • Documentation tightly integrated • A large standard library w/ many add-ons Disadvantages • Execution can be slow • Somewhat decentralized • Different environment, packages and documentation can be spread out at different places. • Can make it harder to get started. • Mitigated by available bundles (e.g. Anaconda) My Reasons Simply put - I enjoy it more. • Open • Many applications I use prefer Python • ArcMap, QGIS, Blender, …. and Linux • By using GDAL, I can open most data types • Growing community • Works well with other languages C, FORTRAN Python Scientific Add-ons Extensive ecosystem of scientific libraries, including: • GDAL – geospatial raster support • numpy: numpy.scipy.org - Numerical Python • scipy: www.scipy.org - Scientific Python • matplotlib: www.matplotlib.org - graphics library What is GDAL? ✤GDAL library is accessible through C, C++, and Python ✤GDAL is the glue that holds everything together ✤ Reads and writes rasters ✤ Converts image, in memory, into a format Numpy arrays ✤ Propagates projection and transformation information ✤ Handles NoData GDAL Data Formats (120+) Arc/Info ASCII Grid Golden Software Surfer 7 Binary Grid Northwood/VerticalMapper Classified
    [Show full text]