Justifying JPEG – JPEG Format Preservation Assessment
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PDF/A for Scanned Documents
Webinar www.pdfa.org PDF/A for Scanned Documents Paper Becomes Digital Mark McKinney, LuraTech, Inc., President Armin Ortmann, LuraTech, CTO Mark McKinney President, LuraTech, Inc. © 2009 PDF/A Competence Center, www.pdfa.org Existing Solutions for Scanned Documents www.pdfa.org Black & White: TIFF G4 Color: Mostly JPEG, but sometimes PNG, BMP and other raster graphics formats Often special version formats like “JPEG in TIFF” Disadvantages: Several formats already for scanned documents Even more formats for born digital documents Loss of information, e.g. with TIFF G4 Bad image quality and huge file size, e.g. with JPEG No standardized metadata spread over all formats Not full text searchable (OCR) inside of files Black/White: Color: - TIFF FAX G4 - TIFF - TIFF LZW Mark McKinney - JPEG President, LuraTech, Inc. - PDF 2 Existing Solutions for Scanned Documents www.pdfa.org Bad image quality vs. file size TIFF/BMP JPEG TIFF G4 23.8 MB 180 kB 60 kB Mark McKinney President, LuraTech, Inc. 3 Alternative Solution: PDF www.pdfa.org PDF is already widely used to: Unify file formats Image à PDF “Office” Documents à PDF Other sources à PDF Create full-text searchable files Apply modern compression technology (e.g. the JPEG2000 file formats family) Harmonize metadata Conclusion: PDF avoids the disadvantages of the legacy formats “So if you are already using PDF as archival Mark McKinney format, why not use PDF/A with its many President, LuraTech, Inc. advantages?” 4 PDF/A www.pdfa.org What is PDF/A? • ISO 19005-1, Document Management • Electronic document file format for long-term preservation Goals of PDF/A: • Maintain static visual representation of documents • Consistent handing of Metadata • Option to maintain structure and semantic meaning of content • Transparency to guarantee access • Limit the number of restrictions Mark McKinney President, LuraTech, Inc. -
Energy-Efficient Design of the Secure Better Portable
Energy-Efficient Design of the Secure Better Portable Graphics Compression Architecture for Trusted Image Communication in the IoT Umar Albalawi Saraju P. Mohanty Elias Kougianos Computer Science and Engineering Computer Science and Engineering Engineering Technology University of North Texas, USA. University of North Texas, USA. University of North Texas. USA. Email: [email protected] Email: [email protected] Email: [email protected] Abstract—Energy consumption has become a major concern it. On the other hand, researchers from the software field in portable applications. This paper proposes an energy-efficient investigate how the software itself and its different uses can design of the Secure Better Portable Graphics Compression influence energy consumption. An efficient software is capable (SBPG) Architecture. The architecture proposed in this paper is suitable for imaging in the Internet of Things (IoT) as the main of adapting to the requirements of everyday usage while saving concentration is on the energy efficiency. The novel contributions as much energy as possible. Software engineers contribute to of this paper are divided into two parts. One is the energy efficient improving energy consumption by designing frameworks and SBPG architecture, which offers encryption and watermarking, tools used in the process of energy metering and profiling [2]. a double layer protection to address most of the issues related to As a specific type of data, images can have a long life if privacy, security and digital rights management. The other novel contribution is the Secure Digital Camera integrated with the stored properly. However, images require a large storage space. SBPG architecture. The combination of these two gives the best The process of storing an image starts with its compression. -
Preparation Method for TIFF File (*.Tif) Over 300Dpi
Preparation method for TIFF file (*.tif) over 300dpi Using software with saving function of TIFF file. (e.g. DeltaGraph) 1. Select the figure. 2. On the “File” menu, point to “Export”, and then select “Image”. 3. Click “Option”, and select “Color/Gray-scale”. 4. Select “TIFF” in the “File type” dialog box, and save the file at over “300”dpi. Using Microsoft Excel. A) Using draw type graphics software. (e.g. Illustrator, Canvas, etc.) 1. Select the figure in Excel. 2. Copy the figure and paste into graphics software. 3. On the “File” menu, point to “Save as”, and save the file after select “TIFF (over 300dpi)“ in the “File type” dialog box. Compression “LZW”, “ZIP”, or “JPEG” should be used in compression mode for TIFF file to reduce the file size. B) Simple method Color printing by Excel or PowerPoint graphics 1. Select the figure in Excel or PowerPoint. 2. On the “File” menu, point to “Print”, and select “Microsoft Office Document Image Writer” under “printer”. Click “Properties”, click the “Advanced” tab, and then check “MDI” under “Output format”. 3. Click “OK”、and then close the “Properties”. 4. Click “OK” under “printer” and save the MDI file. 5. Start Windows Explorer. 6. Open the saved MDI file, or right-click of the saved MDI file —in the “Open with” dialog box; click “Microsoft Office Document Imaging”. 7. On the “Tool” menu, point to “Option”. In the “Compression” tab, check “LZW”, and then click “OK”. 8. On the “File” menu, point to “Save as”, and then select “TIFF ” in the “File type” dialog box. -
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. -
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks Nick Johnston, Damien Vincent, David Minnen, Michele Covell, Saurabh Singh, Troy Chinen, Sung Jin Hwang, Joel Shor, George Toderici {nickj, damienv, dminnen, covell, saurabhsingh, tchinen, sjhwang, joelshor, gtoderici} @google.com, Google Research Abstract formation from the current residual and combines it with context stored in the hidden state of the recurrent layers. By We propose a method for lossy image compression based saving the bits from the quantized bottleneck after each it- on recurrent, convolutional neural networks that outper- eration, the model generates a progressive encoding of the forms BPG (4:2:0), WebP, JPEG2000, and JPEG as mea- input image. sured by MS-SSIM. We introduce three improvements over Our method provides a significant increase in compres- previous research that lead to this state-of-the-art result us- sion performance over previous models due to three im- ing a single model. First, we modify the recurrent architec- provements. First, by “priming” the network, that is, run- ture to improve spatial diffusion, which allows the network ning several iterations before generating the binary codes to more effectively capture and propagate image informa- (in the encoder) or a reconstructed image (in the decoder), tion through the network’s hidden state. Second, in addition we expand the spatial context, which allows the network to lossless entropy coding, we use a spatially adaptive bit to represent more complex representations in early itera- allocation algorithm to more efficiently use the limited num- tions. Second, we add support for spatially adaptive bit rates ber of bits to encode visually complex image regions. -
One Software Solution. One World of Difference for Your Content
Datasheet One software Have you heard? There has been a quiet revolution in solution. One world the way color documents are scanned and published on the Web. It is Document Express with DjVu®--a of diff erence for format that has long been preferred by universities your content. and libraries, because it produces dramatically smaller fi les while preserving original quality. Leading companies around the world are now turning to Document Express including Northwest Airlines, Panasonic, Samsung, Sears, Komatsu, and others-- and that’s because Document Express with DjVu is truly in a class by itself. Only Document Express empowers you to send scanned or electronic color documents on any platform, over any connection speed, with full confi dence in the results. Images download quickly, pages retain true design fi delity, and viewers can access and use your content in ways that are impossible with PDF, TIFF, or JPEG. Document Express with DjVu consistently delivers an excellent user experience, every time. About Document Express with DjVu Features Document Express with DjVu (pronounced: déjà vu) uses a highly effi cient document image compression methodology and fi le format. Scientists at AT&T Labs who fi rst de- veloped the DjVu format for color scanning, also found it vastly superior to Postscript or Sample 400dpi color scan PDF formats for transmitting electronic fi les. Document Express with DjVu uses the most advanced document image segmentation ever developed. The document image seg- 46 MB mentation technology enables the Document Express with DjVu format to have the high- est image quality while keeping text separate to maintain the highest legibility possible. -
Making TIFF Files from Drawing, Word Processing, Powerpoint And
Making TIFF and EPS files from Drawing, Word Processing, PowerPoint and Graphing Programs In the worlds of electronic publishing and video production programs, the need for TIFF or EPS formatted files is a necessity. Unfortunately, most of the imaging work done in research for presen- tation is done in PowerPoint, and this format simply cannot be used in most situations for these three ends. Files can be generally be saved or exported (by using either Save As or Export under File) into TIFF, PICT or JPEG files from PowerPoint, drawing, word processing and graphing programs—all called vector programs—but the results are often poor in resolution (in Photoshop these are shown as having a resolution of 72dpi when opening the Image Size dialogue box: under Image on the menu select Image Size). Here are four ways to save as TIFF (generally the way in which image files are saved) or EPS (gen- erally the way in which files are saved which contain lines or text): Option 1. Use the Program’s Save As or Export option. If it exists, use the Export or Save As option in your vector program. This only works well when a dialogue box appears so that specific values for height, width and resolution can be typed in (as in the programs Canvas and CorelDraw). Anti-aliasing should be checked. Resolution values of 300 dots per inch or pixels per inch is for images, 600 dpi is for images with text and 1200 dpi is for text, graphs and drawings. If no dialogue box exists to type in these values, go to option 2 - 4. -
National Critical Information Infrastructure Protection Centre Common Vulnerabilities and Exposures(CVE) Report
National Critical Information Infrastructure Protection Centre Common Vulnerabilities and Exposures(CVE) Report https://nciipc.gov.in 01 - 15 Mar 2021 Vol. 08 No. 05 Weakness Publish Date CVSS Description & CVE ID Patch NCIIPC ID Application Accellion fta Improper Accellion FTA 9_12_432 Neutralization and earlier is affected by of Special argument injection via a Elements in crafted POST request to an A-ACC-FTA- 02-Mar-21 7.5 N/A Output Used by admin endpoint. The fixed 160321/1 a Downstream version is FTA_9_12_444 Component and later. ('Injection') CVE ID : CVE-2021-27730 Improper Accellion FTA 9_12_432 Neutralization and earlier is affected by of Input During stored XSS via a crafted A-ACC-FTA- Web Page 02-Mar-21 4.3 POST request to a user N/A 160321/2 Generation endpoint. The fixed version ('Cross-site is FTA_9_12_444 and later. Scripting') CVE ID : CVE-2021-27731 adguard adguard_home An issue was discovered in AdGuard before 0.105.2. An Improper attacker able to get the https://githu Restriction of user's cookie is able to b.com/Adgua A-ADG- Excessive 03-Mar-21 5 bruteforce their password rdTeam/AdG ADGU- Authentication offline, because the hash of uardHome/is 160321/3 Attempts the password is stored in sues/2470 the cookie. CVE ID : CVE-2021-27935 Afterlogic webmail_pro Improper 04-Mar-21 6.8 An issue was discovered in https://auror A-AFT- CVSS Scoring Scale 0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 Page 1 of 166 Weakness Publish Date CVSS Description & CVE ID Patch NCIIPC ID Limitation of a AfterLogic Aurora through amail.wordpr WEBM- Pathname to a 8.5.3 and WebMail Pro ess.com/202 160321/4 Restricted through 8.5.3, when DAV is 1/02/03/add Directory enabled. -
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). -
Comparison of JPEG's Competitors for Document Images
Comparison of JPEG’s competitors for document images Mostafa Darwiche1, The-Anh Pham1 and Mathieu Delalandre1 1 Laboratoire d’Informatique, 64 Avenue Jean Portalis, 37200 Tours, France e-mail: fi[email protected] Abstract— In this work, we carry out a study on the per- in [6] concerns assessing quality of common image formats formance of potential JPEG’s competitors when applied to (e.g., JPEG, TIFF, and PNG) that relies on optical charac- document images. Many novel codecs, such as BPG, Mozjpeg, ter recognition (OCR) errors and peak signal to noise ratio WebP and JPEG-XR, have been recently introduced in order to substitute the standard JPEG. Nonetheless, there is a lack of (PSNR) metric. The authors in [3] compare the performance performance evaluation of these codecs, especially for a particular of different coding methods (JPEG, JPEG 2000, MRC) using category of document images. Therefore, this work makes an traditional PSNR metric applied to several document samples. attempt to provide a detailed and thorough analysis of the Since our target is to provide a study on document images, aforementioned JPEG’s competitors. To this aim, we first provide we then use a large dataset with different image resolutions, a review of the most famous codecs that have been considered as being JPEG replacements. Next, some experiments are performed compress them at very low bit-rate and after that evaluate the to study the behavior of these coding schemes. Finally, we extract output images using OCR accuracy. We also take into account main remarks and conclusions characterizing the performance the PSNR measure to serve as an additional quality metric. -
An Improved Objective Metric to Predict Image Quality Using Deep Neural Networks
https://doi.org/10.2352/ISSN.2470-1173.2019.12.HVEI-214 © 2019, Society for Imaging Science and Technology An Improved Objective Metric to Predict Image Quality using Deep Neural Networks Pinar Akyazi and Touradj Ebrahimi; Multimedia Signal Processing Group (MMSPG); Ecole Polytechnique Fed´ erale´ de Lausanne; CH 1015, Lausanne, Switzerland Abstract ages in a full reference (FR) framework, i.e. when the reference Objective quality assessment of compressed images is very image is available, is a difference-based metric called the peak useful in many applications. In this paper we present an objec- signal to noise ratio (PSNR). PSNR and its derivatives do not tive quality metric that is better tuned to evaluate the quality of consider models based on the human visual system (HVS) and images distorted by compression artifacts. A deep convolutional therefore often result in low correlations with subjective quality neural networks is used to extract features from a reference im- ratings. [1]. Metrics such as structural similarity index (SSIM) age and its distorted version. Selected features have both spatial [2], multi-scale structural similarity index (MS-SSIM) [3], feature and spectral characteristics providing substantial information on similarity index (FSIM) [4] and visual information fidelity (VIF) perceived quality. These features are extracted from numerous [5] use models motivated by HVS and natural scenes statistics, randomly selected patches from images and overall image qual- resulting in better correlations with viewers’ opinion. ity is computed as a weighted sum of patch scores, where weights Numerous machine learning based objective quality metrics are learned during training. The model parameters are initialized have been reported in the literature. -
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