Supported File Formats for Images
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Exploring the .BMP File Format
Exploring the .BMP File Format Don Lancaster Synergetics, Box 809, Thatcher, AZ 85552 copyright c2003 as GuruGram #14 http://www.tinaja.com [email protected] (928) 428-4073 The .BMP image standard is used by Windows and elsewhere to represent graphics images in any of several different display and compression options. The .BMP advantages are that each pixel is usually independently available for any alteration or modification. And that repeated use does not normally degrade the image. Because lossy compression is not used. Its main disadvantage is that file sizes are usually horrendous compared to JPEG, fractal, GIF, or other lossy compression schemes. A comparison of popular image standards can be found here. I’ve long been using the .BMP format for my eBay and my other phototography, scanning, and post processing. I firmly believe that… All photography, scanning, and all image post-processing should always be done using .BMP or a similar non-lossy format. Only after all post-processing is complete should JPEG or another compressed distribution format be chosen. Some current examples of my .BMP work now do include the IMAGIMAG.PDF post-processing tutorial, the Bitmap Typewriterthat generates fully anti-aliased small fonts, the Aerial Photo Combiner, and similar utilities and tutorials found on our Fonts & Images, PostScript, and on our Acrobat library pages. A few projects of current interest involving .BMP files include true view camera swings and tilts for a digital camera, distortion correction, dodging & burning, preventing white punchthrough on knockouts, and emphasis vignetting. Mainly applied to uncompressed RGBX 24-bit color .BMP files. -
Free Lossless Image Format
FREE LOSSLESS IMAGE FORMAT Jon Sneyers and Pieter Wuille [email protected] [email protected] Cloudinary Blockstream ICIP 2016, September 26th DON’T WE HAVE ENOUGH IMAGE FORMATS ALREADY? • JPEG, PNG, GIF, WebP, JPEG 2000, JPEG XR, JPEG-LS, JBIG(2), APNG, MNG, BPG, TIFF, BMP, TGA, PCX, PBM/PGM/PPM, PAM, … • Obligatory XKCD comic: YES, BUT… • There are many kinds of images: photographs, medical images, diagrams, plots, maps, line art, paintings, comics, logos, game graphics, textures, rendered scenes, scanned documents, screenshots, … EVERYTHING SUCKS AT SOMETHING • None of the existing formats works well on all kinds of images. • JPEG / JP2 / JXR is great for photographs, but… • PNG / GIF is great for line art, but… • WebP: basically two totally different formats • Lossy WebP: somewhat better than (moz)JPEG • Lossless WebP: somewhat better than PNG • They are both .webp, but you still have to pick the format GOAL: ONE FORMAT THAT COMPRESSES ALL IMAGES WELL EXPERIMENTAL RESULTS Corpus Lossless formats JPEG* (bit depth) FLIF FLIF* WebP BPG PNG PNG* JP2* JXR JLS 100% 90% interlaced PNGs, we used OptiPNG [21]. For BPG we used [4] 8 1.002 1.000 1.234 1.318 1.480 2.108 1.253 1.676 1.242 1.054 0.302 the options -m 9 -e jctvc; for WebP we used -m 6 -q [4] 16 1.017 1.000 / / 1.414 1.502 1.012 2.011 1.111 / / 100. For the other formats we used default lossless options. [5] 8 1.032 1.000 1.099 1.163 1.429 1.664 1.097 1.248 1.500 1.017 0.302� [6] 8 1.003 1.000 1.040 1.081 1.282 1.441 1.074 1.168 1.225 0.980 0.263 Figure 4 shows the results; see [22] for more details. -
Package 'Magick'
Package ‘magick’ August 18, 2021 Type Package Title Advanced Graphics and Image-Processing in R Version 2.7.3 Description Bindings to 'ImageMagick': the most comprehensive open-source image processing library available. Supports many common formats (png, jpeg, tiff, pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc). All operations are vectorized via the Magick++ STL meaning they operate either on a single frame or a series of frames for working with layers, collages, or animation. In RStudio images are automatically previewed when printed to the console, resulting in an interactive editing environment. The latest version of the package includes a native graphics device for creating in-memory graphics or drawing onto images using pixel coordinates. License MIT + file LICENSE URL https://docs.ropensci.org/magick/ (website) https://github.com/ropensci/magick (devel) BugReports https://github.com/ropensci/magick/issues SystemRequirements ImageMagick++: ImageMagick-c++-devel (rpm) or libmagick++-dev (deb) VignetteBuilder knitr Imports Rcpp (>= 0.12.12), magrittr, curl LinkingTo Rcpp Suggests av (>= 0.3), spelling, jsonlite, methods, knitr, rmarkdown, rsvg, webp, pdftools, ggplot2, gapminder, IRdisplay, tesseract (>= 2.0), gifski Encoding UTF-8 RoxygenNote 7.1.1 Language en-US NeedsCompilation yes Author Jeroen Ooms [aut, cre] (<https://orcid.org/0000-0002-4035-0289>) Maintainer Jeroen Ooms <[email protected]> 1 2 analysis Repository CRAN Date/Publication 2021-08-18 10:10:02 UTC R topics documented: analysis . .2 animation . .3 as_EBImage . .6 attributes . .7 autoviewer . .7 coder_info . .8 color . .9 composite . 12 defines . 14 device . 15 edges . 17 editing . 18 effects . 22 fx .............................................. 23 geometry . 24 image_ggplot . -
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs
How to Exploit the Transferability of Learned Image Compression to Conventional Codecs Jan P. Klopp Keng-Chi Liu National Taiwan University Taiwan AI Labs [email protected] [email protected] Liang-Gee Chen Shao-Yi Chien National Taiwan University [email protected] [email protected] Abstract Lossy compression optimises the objective Lossy image compression is often limited by the sim- L = R + λD (1) plicity of the chosen loss measure. Recent research sug- gests that generative adversarial networks have the ability where R and D stand for rate and distortion, respectively, to overcome this limitation and serve as a multi-modal loss, and λ controls their weight relative to each other. In prac- especially for textures. Together with learned image com- tice, computational efficiency is another constraint as at pression, these two techniques can be used to great effect least the decoder needs to process high resolutions in real- when relaxing the commonly employed tight measures of time under a limited power envelope, typically necessitating distortion. However, convolutional neural network-based dedicated hardware implementations. Requirements for the algorithms have a large computational footprint. Ideally, encoder are more relaxed, often allowing even offline en- an existing conventional codec should stay in place, ensur- coding without demanding real-time capability. ing faster adoption and adherence to a balanced computa- Recent research has developed along two lines: evolu- tional envelope. tion of exiting coding technologies, such as H264 [41] or As a possible avenue to this goal, we propose and investi- H265 [35], culminating in the most recent AV1 codec, on gate how learned image coding can be used as a surrogate the one hand. -
Implementation of LSB Steganography and Its Evaluation for Various File Formats (LSB, JSTEG)
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 6, June - 2013 Implementation of LSB Steganography and its Evaluation for Various File Formats (LSB, JSTEG) Vivek Kumar, Sandesh Kumar, Lavalee Singh, Prateek Yadav MANGALAYATAN UNIVERSITY1, 2,3,4 ALIGARH Keywords: Steganography, Least ABSTRACT: Significant Bit (LSB), GIF, PNG, BMP. Steganography is derived from the Greek word steganos which literally means 1. INTRODUCTION “Covered” and graphy means “Writing”, In the current trends of the world, the i.e. covered writing. Steganography refers technologies have advanced so much that to the science of “invisible” Most of the individuals prefer using the communication. For hiding secret internet as the primary medium to transfer information in various file formats, there data from one end to another across the exists a large variety of steganographic IJERTIJERTworld. There are many possible ways to techniques some are more complex than transmit data using the internet: via e-mails, others and all of them have respective chats, etc. The data transition is made very strong and weak points. The Least simple, fast and accurate using the internet. Significant Bit (LSB) embedding However, one of the main problems with technique suggests that data can be sending data over the internet is the security hidden in the least significant bits of the threat it poses i.e. the personal or cover image and the human eye would be confidential data can be stolen or hacked in unable to notice the hidden image in the many ways. Therefore it becomes very cover file. This technique can be used for important to take data security into hiding images in 24-Bit, 8- Bit, Gray scale consideration, as it is one of the most format. -
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. -
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. -
Download This PDF File
Sindh Univ. Res. Jour. (Sci. Ser.) Vol.47 (3) 531-534 (2015) I NDH NIVERSITY ESEARCH OURNAL ( CIENCE ERIES) S U R J S S Performance Analysis of Image Compression Standards with Reference to JPEG 2000 N. MINALLAH++, A. KHALIL, M. YOUNAS, M. FURQAN, M. M. BOKHARI Department of Computer Systems Engineering, University of Engineering and Technology, Peshawar Received 12thJune 2014 and Revised 8th September 2015 Abstract: JPEG 2000 is the most widely used standard for still image coding. Some other well-known image coding techniques include JPEG, JPEG XR and WEBP. This paper provides performance evaluation of JPEG 2000 with reference to other image coding standards, such as JPEG, JPEG XR and WEBP. For the performance evaluation of JPEG 2000 with reference to JPEG, JPEG XR and WebP, we considered divers image coding scenarios such as continuous tome images, grey scale images, High Definition (HD) images, true color images and web images. Each of the considered algorithms are briefly explained followed by their performance evaluation using different quality metrics, such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structure Similarity Index (SSIM), Bits Per Pixel (BPP), Compression Ratio (CR) and Encoding/ Decoding Complexity. The results obtained showed that choice of the each algorithm depends upon the imaging scenario and it was found that JPEG 2000 supports the widest set of features among the evaluated standards and better performance. Keywords: Analysis, Standards JPEG 2000. performance analysis, followed by Section 6 with 1. INTRODUCTION There are different standards of image compression explanation of the considered performance analysis and decompression. -
Understanding Image Formats and When to Use Them
Understanding Image Formats And When to Use Them Are you familiar with the extensions after your images? There are so many image formats that it’s so easy to get confused! File extensions like .jpeg, .bmp, .gif, and more can be seen after an image’s file name. Most of us disregard it, thinking there is no significance regarding these image formats. These are all different and not cross‐ compatible. These image formats have their own pros and cons. They were created for specific, yet different purposes. What’s the difference, and when is each format appropriate to use? Every graphic you see online is an image file. Most everything you see printed on paper, plastic or a t‐shirt came from an image file. These files come in a variety of formats, and each is optimized for a specific use. Using the right type for the right job means your design will come out picture perfect and just how you intended. The wrong format could mean a bad print or a poor web image, a giant download or a missing graphic in an email Most image files fit into one of two general categories—raster files and vector files—and each category has its own specific uses. This breakdown isn’t perfect. For example, certain formats can actually contain elements of both types. But this is a good place to start when thinking about which format to use for your projects. Raster Images Raster images are made up of a set grid of dots called pixels where each pixel is assigned a color. -
JPEG Image Compression.Pdf
JPEG image compression FAQ, part 1/2 2/18/05 5:03 PM Part1 - Part2 - MultiPage JPEG image compression FAQ, part 1/2 There are reader questions on this topic! Help others by sharing your knowledge Newsgroups: comp.graphics.misc, comp.infosystems.www.authoring.images From: [email protected] (Tom Lane) Subject: JPEG image compression FAQ, part 1/2 Message-ID: <[email protected]> Summary: General questions and answers about JPEG Keywords: JPEG, image compression, FAQ, JPG, JFIF Reply-To: [email protected] Date: Mon, 29 Mar 1999 02:24:27 GMT Sender: [email protected] Archive-name: jpeg-faq/part1 Posting-Frequency: every 14 days Last-modified: 28 March 1999 This article answers Frequently Asked Questions about JPEG image compression. This is part 1, covering general questions and answers about JPEG. Part 2 gives system-specific hints and program recommendations. As always, suggestions for improvement of this FAQ are welcome. New since version of 14 March 1999: * Expanded item 10 to discuss lossless rotation and cropping of JPEGs. This article includes the following sections: Basic questions: [1] What is JPEG? [2] Why use JPEG? [3] When should I use JPEG, and when should I stick with GIF? [4] How well does JPEG compress images? [5] What are good "quality" settings for JPEG? [6] Where can I get JPEG software? [7] How do I view JPEG images posted on Usenet? More advanced questions: [8] What is color quantization? [9] What are some rules of thumb for converting GIF images to JPEG? [10] Does loss accumulate with repeated compression/decompression? -
Vue PACS 12.2 DICOM Conformance Statement
Clinical Collaboration Platform Vue PACS 12.2 DICOM Conformance Statement Part # AD0536 2017-11-23 Vue PACS 12.2 DICOM Conformance Statement AD0536 A Uncontrolled unless otherwise indicated Confidential Vue PACS 12.2 - DICOM Conformance Statement - AD0536.docx PAGE 1 of 155 Table of Contents 1 Introduction ............................................................................................................................................ 3 1.1 Terms and Definitions ...................................................................................................................... 3 1.2 About This Document ....................................................................................................................... 4 1.3 Important Remarks ........................................................................................................................... 4 2 Implementation Model............................................................................................................................ 5 2.1 Application Data Flow Diagram ........................................................................................................ 5 2.2 Functional Definitions of AEs ......................................................................................................... 11 2.3 Sequencing of Real World Activities .............................................................................................. 12 3 AE Specifications ................................................................................................................................ -
The Rehabilitation of Gamma
The rehabilitation of gamma Charles Poynton poynton @ poynton.com www.poynton.com Abstract Gamma characterizes the reproduction of tone scale in an imaging system. Gamma summarizes, in a single numerical parameter, the nonlinear relationship between code value – in an 8-bit system, from 0 through 255 – and luminance. Nearly all image coding systems are nonlinear, and so involve values of gamma different from unity. Owing to poor understanding of tone scale reproduction, and to misconceptions about nonlinear coding, gamma has acquired a terrible reputation in computer graphics and image processing. In addition, the world-wide web suffers from poor reproduction of grayscale and color images, due to poor handling of nonlinear image coding. This paper aims to make gamma respectable again. Gamma’s bad reputation The left-hand column in this table summarizes the allegations that have led to gamma’s bad reputation. But the reputation is ill-founded – these allegations are false! In the right column, I outline the facts: Misconception Fact A CRT’s phosphor has a nonlinear The electron gun of a CRT is responsible for its nonlinearity, not the phosphor. response to beam current. The nonlinearity of a CRT monitor The nonlinearity of a CRT is very nearly the inverse of the lightness sensitivity is a defect that needs to be of human vision. The nonlinearity causes a CRT’s response to be roughly corrected. perceptually uniform. Far from being a defect, this feature is highly desirable. The main purpose of gamma The main purpose of gamma correction in video, desktop graphics, prepress, JPEG, correction is to compensate for the and MPEG is to code luminance or tristimulus values (proportional to intensity) into nonlinearity of the CRT.