Application of Reversible Denoising and Lifting Steps to DWT in Lossless JPEG 2000 for Improved Bitrates
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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. -
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 ................................................................................................................................ -
CP2100 Clarify SMPTE Transfer Syntaxes for DICOM Web Services
16 CP-2100 - Clarify SMPTE Transfer Syntaxes for DICOM Web Services Page 1 1 Status Final Text 2 Date of Last Update 2021/09/03 3 Person Assigned David Clunie 4 mailto:[email protected] 5 Submitter Name Mathieu Malaterre 6 mailto:[email protected] 7 Submission Date 2020/04/16 8 Correction Number CP-2100 9 Log Summary: Clarify SMPTE Transfer Syntaxes for DICOM Web Services 10 Name of Standard 11 PS3.18 12 Rationale for Correction: 13 Clarify that the video Transfer Syntaxes used for RTV, such as SMPTE ST 2110-20, are not applicable. 14 Correction Wording: 15 - Final Text - 48 CP-2100 - Clarify SMPTE Transfer Syntaxes for DICOM Web Services Page 2 1 Amend DICOM PS3.18 as follows (changes to existing text are bold and underlined for additions and struckthrough for removals): 2 8.7.3.1 Instance Media Types 3 The application/dicom media type specifies a representation of Instances encoded in the DICOM File Format specified in Section 7 4 “DICOM File Format” in PS3.10. 5 Note 6 The origin server may populate the PS3.10 File Meta Information with the identification of the Source, Sending and Receiving 7 AE Titles and Presentation Addresses as described in Section 7.1 in PS3.10, or these Attributes may have been left unaltered 8 from when the origin server received the objects. The user agent storing the objects received in the response may populate 9 or coerce these Attributes based on its own knowledge of the endpoints involved in the transaction, so that they accurately 10 identify the most recent -
Learning Scalable Ly=-Constrained Near-Lossless Image Compression Via Joint Lossy Image and Residual Compression
Learning Scalable ℓ∞-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression Yuanchao Bai1, Xianming Liu1,2,∗ Wangmeng Zuo1,2, Yaowei Wang1, Xiangyang Ji3 1Peng Cheng Laboratory, 2Harbin Institute of Technology, 3Tsinghua University {baiych,wangyw}@pcl.ac.cn, {csxm,wmzuo}@hit.edu.cn, [email protected] Abstract of the decoded images, ℓ∞-constrained near-lossless image compression is developed [6, 15, 2, 47, 51] and standardized We propose a novel joint lossy image and residual in traditional codecs, e.g., JPEG-LS [47] and CALIC [51]. compression framework for learning ℓ∞-constrained near- Different from lossy image compression with Peak Signal- lossless image compression. Specifically, we obtain a lossy to-Noise Ratio (PSNR) or Multi-Scale Structural SIMilar- reconstruction of the raw image through lossy image com- ity index (MS-SSIM) [45, 46] distortion measures, ℓ∞- pression and uniformly quantize the corresponding resid- constrained near-lossless image compression requires the ual to satisfy a given tight ℓ∞ error bound. Suppose that maximum reconstruction error of each pixel to be no larger the error bound is zero, i.e., lossless image compression, than a given tight numerical bound. Lossless image com- we formulate the joint optimization problem of compress- pression is a special case of near-lossless image compres- ing both the lossy image and the original residual in terms sion, when the tight error bound is zero. of variational auto-encoders and solve it with end-to-end With the fast development of deep neural networks training. To achieve scalable compression with the error (DNNs), learning-based lossy image compression [40, 3, bound larger than zero, we derive the probability model 39, 41, 34, 24, 4, 31, 27, 22, 8, 7, 23, 25, 29] has achieved of the quantized residual by quantizing the learned prob- tremendous progress over the last four years. -
Still Image Compression Standards
Still Image Compression Standards Michael W. Hoffman and Khalid Sayood Presented by: Jafar Ajdari Content 5.1 Introduction 5.2 Lossy compression 5.2.1 JPEG 5.2.1.1 DCT-Bsed Image Compression 5.2.1.2 Progressive Transmission 5.2.1.3 General Syntax and Data Ordering 5.2.1.3 Entropy Coding 5.2.2 JPEG2000 5.3 lossless Compression 5.3.1 JPEG 5.3.2 JPEG-LS 5.4 Bilevel Image Compression 5.4.1 JBIG 5.4.2 JBIG2 Definitions of some key terms DCT: Discrete Cosine Transform. JPEG: Joint Photographic Expert Group. JPEG200: the current standard that emphasizes lossy compression of images. JPEG-LS: An upcoming standard that focuses on lossless and near-lossless compression of still images JBIG: Joint Bilevel Image Group. Wavelets: A time-scale decomposition that allow very efficient energy compaction in images. INTRODUCTION What is image compression? Image data can be compressed without significant degradation of the visual (perceptual) quality b/c image contain a high degree of: • Spatial redundancy • Spectral redundancyPsycho-visual redundancy Why Standardization? Compression is one of the technologies that enable the multimedia revolution to occur. However for technology to be effective there has to be some degree of standardization so that the equipment designed by different vendors can talk to each other. Type of still image compression standards: • (JPEG) Joint Photographic Experts Group a- Lossy copression of still images b- Lossless compression of still images • (JBIG) Joint Bilevel Image Group • (GIF) Graphics Interchange Format. de facto • (PNG) Portable Network Graphics. De facto Compression scheme In any compression scheme there are: Step 1- Removal of redundancy based on implicit assumption about the structure in the data Step 2- Assignment of binary codewords to the information deemed nonredundant. -
Benchmarking JPEG XL Image Compression
Benchmarking JPEG XL image compression a a c a b Jyrki Alakuijala , Sami Boukortt , Touradj Ebrahimi , Evgenii Kliuchnikov , Jon Sneyers , Evgeniy Upenikc, Lode Vandevennea, Luca Versaria, Jan Wassenberg1*a a b Google Switzerland, 8002 Zurich, Switzerland; Cloudinary, Santa Clara, CA 95051, USA; c Multimedia Signal Processing Group, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. ABSTRACT JPEG XL is a practical, royalty-free codec for scalable web distribution and efficient compression of high-quality photographs. It also includes previews, progressiveness, animation, transparency, high dynamic range, wide color gamut, and high bit depth. Users expect faithful reproductions of ever higher-resolution images. Experiments performed during standardization have shown the feasibility of economical storage without perceptible quality loss, lossless transcoding of existing JPEG, and fast software encoders and decoders. We analyse the results of subjective and objective evaluations versus existing codecs including HEVC and JPEG. New image codecs have to co-exist with their previous generations for several years. JPEG XL is unique in providing value for both existing JPEGs as well as new users. It includes coding tools to reduce the transmission and storage costs of JPEG by 22% while allowing byte-for-byte exact reconstruction of the original JPEG. Avoiding transcoding and additional artifacts helps to preserve our digital heritage. Applications require fast and low-power decoding. JPEG XL was designed to benefit from multicore and SIMD, and actually decodes faster than JPEG. We report the resulting speeds on ARM and x86 CPUs. To enable reproduction of these results, we open sourced the JPEG XL software in 2019. Keywords: image compression, JPEG XL, perceptual visual quality, image fidelity 1. -
JPEG 2000 - a Practical Digital Preservation Standard?
Technology Watch Report JPEG 2000 - a Practical Digital Preservation Standard? Robert Buckley, Ph.D. Xerox Research Center Webster Webster, New York DPC Technology Watch Series Report 08-01 February 2008 © Digital Preservation Coalition 2008 1 Executive Summary JPEG 2000 is a wavelet-based standard for the compression of still digital images. It was developed by the ISO JPEG committee to improve on the performance of JPEG while adding significant new features and capabilities to enable new imaging applications. The JPEG 2000 compression method is part of a multi-part standard that defines a compression architecture, file format family, client-server protocol and other components for advanced applications. Instead of replacing JPEG, JPEG 2000 has created new opportunities in geospatial and medical imaging, digital cinema, image repositories and networked image access. These opportunities are enabled by the JPEG 2000 feature set: • A single architecture for lossless and visually lossless image compression • A single JPEG 2000 master image can supply multiple derivative images • Progressive display, multi-resolution imaging and scalable image quality • The ability to handle large and high-dynamic range images • Generous metadata support With JPEG 2000, an application can access and decode only as much of the compressed image as needed to perform the task at hand. This means a viewer, for example, can open a gigapixel image almost instantly by retrieving and decompressing a low resolution, display-sized image from the JPEG 2000 codestream. JPEG 2000 also improves a user’s ability to interact with an image. The zoom, pan, and rotate operations that users increasingly expect in networked image systems are performed dynamically by accessing and decompressing just those parts of the JPEG 2000 codestream containing the compressed image data for the region of interest. -
A New Lossless Image Compression Algorithm Based on Arithmetic Coding
A New Lossless Image Compression Algorithm Based on Arithmetic Coding Bruno Carpentieri Dipartimento di Informatica ed Applicazioni "R. 3/1.. Capocelli" Universita' di Salerno 84081 Baronissi (SA), ltaly Abstract We present a new lossless image compression algorithm based on Arithmetic Coding. Our algorithm seleots appropriately, for each pixel position, one of a large number of possible, d3mamic , probability dish-ibutions, and encodes the current pixel prediction error by using this distribution as the model for the arithmetic encoder. We have experimentally compared our algorithm with Lossless JPEG, that is currently the lossless image compression standard, and also with FELICS and other lossless compression algorithms. Our tests show that the new algorithm outperforms Lossless JPEG and FELICS leading to a compression improvement of about 12% over Lossless JPEG and 10% over FF~LICS. I Introduction Compression is the coding of data to minimize its representation. The compression of images is motivated by the economic and logistic needs to conserve space in storage media and to save bandwidth in communication. In compressed form data can be stored more compactly and transmitted more rapidly. There are two basic reasons for expecting to be able to compress images. First, the information itself contains redundancies in the form of non uniform distribution of signal values (spatial correlation). Second, the act of digitizing contributes an expansion. The compression process is called lossless compression (also reversible or noiseless coding or redundancy reduction) if the original can be exactly reconstructed from the compressed copy; otherwise it is called los~y compression (also irreversible or fidelity-reducing coding or entropy reduction). -
The Jpeg2000 Still Image Coding System: an Overview
Published in IEEE Transactions on Consumer Electronics, Vol. 46, No. 4, pp. 1103-1127, November 2000 THE JPEG2000 STILL IMAGE CODING SYSTEM: AN OVERVIEW Charilaos Christopoulos1 Senior Member, IEEE, Athanassios Skodras2 Senior Member, IEEE, and Touradj Ebrahimi3 Member, IEEE 1Media Lab, Ericsson Research Corporate Unit, Ericsson Radio Systems AB, S-16480 Stockholm, Sweden Email: [email protected] 2Electronics Laboratory, University of Patras, GR-26110 Patras, Greece Email: [email protected] 3Signal Processing Laboratory, EPFL, CH-1015 Lausanne, Switzerland Email: [email protected] Abstract -- With the increasing use of multimedia international standard for the compression of technologies, image compression requires higher grayscale and color still images. This effort has been performance as well as new features. To address this known as JPEG, the Joint Photographic Experts need in the specific area of still image encoding, a new Group the “joint” in JPEG refers to the collaboration standard is currently being developed, the JPEG2000. It between ITU and ISO). Officially, JPEG corresponds is not only intended to provide rate-distortion and subjective image quality performance superior to to the ISO/IEC international standard 10928-1, digital existing standards, but also to provide features and compression and coding of continuous-tone functionalities that current standards can either not (multilevel) still images or to the ITU-T address efficiently or in many cases cannot address at Recommendation T.81. The text in both these ISO and all. Lossless and lossy compression, embedded lossy to ITU-T documents is identical. The process was such lossless coding, progressive transmission by pixel that, after evaluating a number of coding schemes, the accuracy and by resolution, robustness to the presence JPEG members selected a DCT1-based method in of bit-errors and region-of-interest coding, are some 1988. -
Lossless Data Compression of Grid-Based Digital Elevation Models: a Png Image Format Evaluation
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014 ISPRS Technical Commission V Symposium, 23 – 25 June 2014, Riva del Garda, Italy LOSSLESS DATA COMPRESSION OF GRID-BASED DIGITAL ELEVATION MODELS: A PNG IMAGE FORMAT EVALUATION G. Scarmana University of Southern Queensland, Australia [email protected] Commission V KEY WORDS: Image processing; DEM compression; Terrain modelling; PNG (Portable Networks Graphics). ABSTRACT: At present, computers, lasers, radars, planes and satellite technologies make possible very fast and accurate topographic data acquisition for the production of maps. However, the problem of managing and manipulating this data efficiently remains. One particular type of map is the elevation map. When stored on a computer, it is often referred to as a Digital Elevation Model (DEM). A DEM is usually a square matrix of elevations. It is like an image, except that it contains a single channel of information (that is, elevation) and can be compressed in a lossy or lossless manner by way of existing image compression protocols. Compression has the effect of reducing memory requirements and speed of transmission over digital links, while maintaining the integrity of data as required. In this context, this paper investigates the effects of the PNG (Portable Network Graphics) lossless image compression protocol on floating-point elevation values for 16-bit DEMs of dissimilar terrain characteristics. The PNG is a robust, universally supported, extensible, lossless, general-purpose and patent-free image format. Tests demonstrate that the compression ratios and run decompression times achieved with the PNG lossless compression protocol can be comparable to, or better than, proprietary lossless JPEG variants, other image formats and available lossless compression algorithms. -
A LINE-BASED LOSSLESS BACKWARD CODING of WAVELET TREES (BCWT) and BCWT IMPROVEMENTS for APPLICATION by Bian Li, B.S.E.E., M.S.E.E
A LINE-BASED LOSSLESS BACKWARD CODING OF WAVELET TREES (BCWT) AND BCWT IMPROVEMENTS FOR APPLICATION by Bian Li, B.S.E.E., M.S.E.E. A Dissertation In ELECTRICAL ENGINEERING Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Approved Dr. Brian Nutter Chair of Committee Dr. Sunanda Mitra Co-Chair of Committee Dr. Ranadip Pal Dr. Mark Sheridan Dean of the Graduate School May 2014 ®2014 Bian Li All Rights Reserved Texas Tech University, Bian Li, May 2014 ACKNOWLEDGEMENTS Countless people deserve a heartfelt of thanks from the earnest bottom of my heart. First and foremost, I would like to gratefully acknowledge the supervision of Dr. Brian Nutter. His extraordinary knowledge and proficiencies help me understand a complex world in a simple way. He has provided me precious advice, wonderful teaching, creative ideas and great opportunities. I would have been lost without him. I am extremely grateful to Dr. Sunanda Mitra. She provides me great suggestions on carrying out research and organizing paper and gives continuous living support with her resources. Thank Dr. Ranadip Pal for providing me scientific suggestions. Dr. Mary Baker also has my special appreciation for her substantial support. I thank for the classes and help from the faculties and staffs at Electrical Engineering and Computer Science. My special thanks go to my friends and labmates. We had a wonderful time in Texas Tech. In particular, I wish to thank Dan Mclane and Chunmei Kang in Chirp Inc. for the financial support and collaboration for the dissertation.