Making Color Images with the GIMP Las Cumbres Observatory Global Telescope Network Color Imaging: the GIMP Introduction
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Panstamps Documentation Release V0.5.3
panstamps Documentation Release v0.5.3 Dave Young 2020 Getting Started 1 Installation 3 1.1 Troubleshooting on Mac OSX......................................3 1.2 Development...............................................3 1.2.1 Sublime Snippets........................................4 1.3 Issues...................................................4 2 Command-Line Usage 5 3 Documentation 7 4 Command-Line Tutorial 9 4.1 Command-Line..............................................9 4.1.1 JPEGS.............................................. 12 4.1.2 Temporal Constraints (Useful for Moving Objects)...................... 17 4.2 Importing to Your Own Python Script.................................. 18 5 Installation 19 5.1 Troubleshooting on Mac OSX...................................... 19 5.2 Development............................................... 19 5.2.1 Sublime Snippets........................................ 20 5.3 Issues................................................... 20 6 Command-Line Usage 21 7 Documentation 23 8 Command-Line Tutorial 25 8.1 Command-Line.............................................. 25 8.1.1 JPEGS.............................................. 28 8.1.2 Temporal Constraints (Useful for Moving Objects)...................... 33 8.2 Importing to Your Own Python Script.................................. 34 8.2.1 Subpackages.......................................... 35 8.2.1.1 panstamps.commonutils (subpackage)........................ 35 8.2.1.2 panstamps.image (subpackage)............................ 35 8.2.2 Classes............................................ -
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. -
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). -
Frequently Asked Questions
Frequently Asked Questions After more than 16 years of business, we’ve run into a lot of questions from a lot of different people, each with different needs and requirements. Sometimes the questions are about our company or about the exact nature of dynamic imaging, but, no matter the question, we’re happy to answer and discuss any imaging solutions that may help the success of your business. So take a look... we may have already answered your question. If we haven’t, please feel free to contact us at www.liquidpixels.com. How long has LiquidPixels been in business? Do you have an API? LiquidPixels started in 2000 and has been going strong since LiquiFire OS uses our easy-to-learn LiquiFire® Image Chains to then, bringing dynamic imaging to the world of e-commerce. describe the imaging manipulations you wish to produce. Can you point me to live websites that use your services? What is the average time required to implement LiquiFire OS LiquidPixels’ website features some of our customers who into my environment? benefit from the LiquiFire® Operating System (OS). Our Whether you choose our turnkey LiquiFire Imaging Server™ sales team will be happy to showcase existing LiquiFire OS solutions or our LiquiFire Hosted Service™, LiquiFire OS can solutions or show you how incredible your website and be implemented in less than a day. Then your creative team can product imagery would look with the power of LiquiFire OS get to the exciting part: developing your website’s interface, behind it. presentation, and workflow. What types of images are required (file format, quality, etc.)? LiquiFire OS supports all common image formats, including JPEG, PNG, TIFF, WebP, JXR, and GIF and also allows for PostScript and PDF manipulation. -
Digital Recording of Performing Arts: Formats and Conversion
detailed approach also when the transfer of rights forms part of an employment contract between the producer of Digital recording of performing the recording and the live crew. arts: formats and conversion • Since the area of activity most probably qualifies as Stijn Notebaert, Jan De Cock, Sam Coppens, Erik Mannens, part of the ‘cultural sector’, separate remuneration for Rik Van de Walle (IBBT-MMLab-UGent) each method of exploitation should be stipulated in the Marc Jacobs, Joeri Barbarien, Peter Schelkens (IBBT-ETRO-VUB) contract. If no separate remuneration system has been set up, right holders might at any time invoke the legal default mechanism. This default mechanism grants a proportionate part of the gross revenue linked to a specific method of exploitation to the right holders. The producer In today’s digital era, the cultural sector is confronted with a may also be obliged to provide an annual overview of the growing demand for making digital recordings – audio, video and gross revenue per way of exploitation. This clause is crucial still images – of stage performances available over a multitude of in order to avoid unforeseen financial and administrative channels, including digital television and the internet. Essentially, burdens in a later phase. this can be accomplished in two different ways. A single entity can act as a content aggregator, collecting digital recordings from • Determine geographical scope and, if necessary, the several cultural partners and making this content available to duration of the transfer for each way of exploitation. content distributors or each individual partner can distribute its own • Include future methods of exploitation in the contract. -
Designing and Developing a Model for Converting Image Formats Using Java API for Comparative Study of Different Image Formats
International Journal of Scientific and Research Publications, Volume 4, Issue 7, July 2014 1 ISSN 2250-3153 Designing and developing a model for converting image formats using Java API for comparative study of different image formats Apurv Kantilal Pandya*, Dr. CK Kumbharana** * Research Scholar, Department of Computer Science, Saurashtra University, Rajkot. Gujarat, INDIA. Email: [email protected] ** Head, Department of Computer Science, Saurashtra University, Rajkot. Gujarat, INDIA. Email: [email protected] Abstract- Image is one of the most important techniques to Different requirement of compression in different area of image represent data very efficiently and effectively utilized since has produced various compression algorithms or image file ancient times. But to represent data in image format has number formats with time. These formats includes [2] ANI, ANIM, of problems. One of the major issues among all these problems is APNG, ART, BMP, BSAVE, CAL, CIN, CPC, CPT, DPX, size of image. The size of image varies from equipment to ECW, EXR, FITS, FLIC, FPX, GIF, HDRi, HEVC, ICER, equipment i.e. change in the camera and lens puts tremendous ICNS, ICO, ICS, ILBM, JBIG, JBIG2, JNG, JPEG, JPEG 2000, effect on the size of image. High speed growth in network and JPEG-LS, JPEG XR, MNG, MIFF, PAM, PCX, PGF, PICtor, communication technology has boosted the usage of image PNG, PSD, PSP, QTVR, RAS, BE, JPEG-HDR, Logluv TIFF, drastically and transfer of high quality image from one point to SGI, TGA, TIFF, WBMP, WebP, XBM, XCF, XPM, XWD. another point is the requirement of the time, hence image Above mentioned formats can be used to store different kind of compression has remained the consistent need of the domain. -
Mapexif: an Image Scanning and Mapping Tool for Investigators
MAPEXIF: AN IMAGE SCANNING AND MAPPING TOOL 1 MapExif: an image scanning and mapping tool for investigators Lionel Prat NhienAn LeKhac Cheryl Baker University College Dublin, Belfield, Dublin 4, Ireland MAPEXIF: AN IMAGE SCANNING AND MAPPING TOOL 2 Abstract Recently, the integration of geographical coordinates into a picture has become more and more popular. Indeed almost all smartphones and many cameras today have a built-in GPS receiver that stores the location information in the Exif header when a picture is taken. Although the automatic embedding of geotags in pictures is often ignored by smart phone users as it can lead to endless discussions about privacy implications, these geotags could be really useful for investigators in analysing criminal activity. Currently, there are many free tools as well as commercial tools available in the market that can help computer forensics investigators to cover a wide range of geographic information related to criminal scenes or activities. However, there are not specific forensic tools available to deal with the geolocation of pictures taken by smart phones or cameras. In this paper, we propose and develop an image scanning and mapping tool for investigators. This tool scans all the files in a given directory and then displays particular photos based on optional filters (date/time/device/localisation…) on Google Map. The file scanning process is not based on the file extension but its header. This tool can also show efficiently to users if there is more than one image on the map with the same GPS coordinates, or even if there are images with no GPS coordinates taken by the same device in the same timeline. -
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 -
CONVERT – a Format-Conversion Package Version 1.8 User’S Manual SUN/55.32 —Abstract Ii
SUN/55.32 Starlink Project Starlink User Note 55.32 Malcolm J. Currie G.J.Privett A.J.Chipperfield D.S.Berry A.C.Davenhall 2015 December 14 Copyright c 2015 Science and Technology Research Council CONVERT – A Format-conversion Package Version 1.8 User’s Manual SUN/55.32 —Abstract ii Abstract The CONVERT package contains utilities for converting data files between Starlink’s Extensible n-dimensional Data Format (NDF), which is used by most Starlink applications, and a number of other common data formats. Using these utilities, astronomers can process their data selecting the best applications from a variety of Starlink or other packages. Most of the CONVERT utilities may be run from the shell or ICL in the normal way, or invoked automatically by the NDF library’s ‘on-the-fly’ data-conversion system, but there are also IDL procedures for handling NDFs from within IDL. Copyright c 2015 Science and Technology Research Council iii SUN/55.32—Contents Contents 1 Introduction 1 2 Running CONVERT 4 2.1 Starting CONVERT from the UNIX shell . 4 2.2 Starting CONVERT from ICL ............................... 4 2.3 Issuing CONVERT Commands . 5 2.4 Obtaining Help . 6 2.5 Hypertext Help . 7 3 Automatic Format Conversion with the NDF Library 7 3.1 The Default Conversion Commands . 7 4 Automatic HISTORY Creation 9 5 Acknowledgments 9 A Specifications of CONVERT Applications 11 A.1 Explanatory Notes . 11 ASCII2NDF . 13 AST2NDF . 16 DA2NDF . 17 DST2NDF . 19 FITS2NDF . 22 GASP2NDF . 38 GIF2NDF . 40 IRAF2NDF . 41 IRCAM2NDF . 44 MTFITS2NDF . 48 NDF2ASCII . 50 NDF2DA . -
Using the FITS Data Structure
Stephen Finniss (FNNSTE003) Literature Synthesis Using the FITS Data Structure Abstract In the interest of implementing a back-end application that can handle FITS data, an introduction into what the FITS file format is, and what it aims to achieve is presented. To understand the contents of a FITS file a brief overview of the structure of FITS files is conducted. In addition a description of standard and conforming extensions to the FITS format are briefly investigated to demonstrate the flexibility of the format. To demonstrate the usefulness of the format an overview of what data is recorded using the format, as well as the number of locations produces files in this format is shown. The results from a brief investigation into what FITS IO libraries are available is discussed. The main conclusions are that writing a library for handling IO is a large task, and using CFITSIO is advised and that due to how many different kinds of information can be stored using the format that targeting a subset would allow for a better application to be written. Introduction The FITS file format was defined as a way to allow astronomical information to be transported between institutions which all use different internal formats. To achieve this goal the format itself is very generic, and can be used to represent a large amount of different data types. Furthermore, all valid FITS files will remain valid even when updates are made to the FITS standard[1]. Implementing an application which uses these files is an interesting task as the flexibility of the format means that there are many ways to interpret the data contained in them. -
High Efficiency Image File Format Implementation
LASSE HEIKKILÄ HIGH EFFICIENCY IMAGE FILE FORMAT IMPLEMENTATION Master of Science thesis Examiner: Prof. Petri Ihantola Examiner and topic approved by the Faculty Council of the Faculty of Computing and Electrical Engineering on 4th of May 2016 i ABSTRACT LASSE HEIKKILÄ: High Efficiency Image File Format implementation Tampere University of Technology Master of Science thesis, 49 pages, 1 Appendix page June 2016 Master’s Degree Programme in Electrical Engineering Technology Major: Embedded systems Examiner: Prof. Petri Ihantola Keywords: High Efficiency Image File Format, HEIF, HEVC During recent years, methods used to encode video have been developing quickly. However, image file formats commonly used for saving still images, such as PNG and JPEG, are originating from the 1990s. Therefore it is often possible to get better image quality and smaller file sizes, when photographs are compressed with modern video compression techniques. The High Efficiency Video Coding (HEVC) standard was finalized in 2013, and in the same year work for utilizing it for still image storage started. The resulting High Efficiency Image File Format (HEIF) standard offers a competitive image data compression ratio, and several other features such as support for image sequences and non-destructive editing. During this thesis work, writer and reader programs for handling HEIF files were developed. Together with an HEVC encoder the writer can create HEIF compliant files. By utilizing the reader and an HEVC decoder, an HEIF player program can then present images from HEIF files without the most detailed knowledge about their low-level structure. To make development work easier, and improve the extensibility and maintainability of the programs, code correctness and simplicity were given special attention. -
(EOSSA) DATA PRODUCTS Tamara Payne, Shaylah Mutschler, Neal S
A COMMUNITY FORMAT FOR ELECTRO-OPTICAL SPACE SITUATIONAL AWARENESS (EOSSA) DATA PRODUCTS Tamara Payne, Shaylah Mutschler, Neal Shine Applied Optimization Daniel Meiser, Roberto Crespo NASIC/GSMS Elizabeth Beecher, Lawrence Schmitt AFRL/RYWW ABSTRACT In this paper, we present a flexible format for compiling radiometry/photometry data with pertinent information about the collections into a file for use by the Space Situational Awareness (SSA) community. With the increase in the number of Electro-Optical (EO) sensors collecting photometric, radiometric, and spectroscopic data on man-made Resident Space Objects (RSOs) for SSA purposes, the EO SSA community of interest and stakeholders in SSA require a file format protocol for reporting the extracted information used for SSA from these datasets. This EOSSA file format provides a foundation to enable data providers to format their processed data. The objective of this format is to handle a variety of photometric measurements from multiple sensors and provide fields for specific parameters containing crucial data about the object, the sensor, the collection, and the processing. The chosen formatting type for EOSSA is the Flexible Image Transport System (FITS). It is maintained by the International Astronomical Union and NASA/GSFC. FITS is the standard data format used in astronomy and has extensions and features that make it easy to transport and archive large scientific data sets. There are types of FITS files for multi-dimensional arrays, such as images, or hyperspectral image cubes, and headers and tables for data extracted from the images, and descriptive information about the data and sensor. The FITS binary table extension is the most efficient data structure to use for the purposes of SSA with respect to ease of programming, computational speed, and storage space.