Lossy Image Compression

Image compression is concerned with minimization of the number of information carrying units used to represent an image. Lossy compression techniques incur some loss of information which is usually imperceptible. In return for accepting this distortion, we obtain much higher compression ratios than is possible with lossless compression. Salient features of this book include: four new image compression algorithms and implementation of these algorithms; detailed discussion of fuzzy geometry measures and their application in image compression algorithms; new domain decomposition based algorithms using image quality measures and study of various quality measures for gray scale image compression; compression algorithms for different parallel architectures and evaluation of time complexity for encoding on all architectures; parallel implementation of image compression algorithms on a cluster in Parallel Virtual Machine (PVM) environment.

Image compression is concerned with minimization of the number of information carrying units used to represent an image. Lossy compression techniques incur some loss of information which is usually imperceptible. In return for accepting this distortion, we obtain much higher compression ratios than is possible with lossless compression. Salient features of this book include: four new image compression algorithms and implementation of these algorithms; detailed discussion of fuzzy geometry measures and their application in image compression algorithms; new domain decomposition based algorithms using image quality measures and study of various quality measures for gray scale image compression; compression algorithms for different parallel architectures and evaluation of time complexity for encoding on all architectures; parallel implementation of image compression algorithms on a cluster in Parallel Virtual Machine (PVM) environment.

In order to utilize digital images effectively, specific techniques are needed to reduce the number of bits required for their representation. This Tutorial Text provides the groundwork for understanding these image compression tecniques and presents a number of different schemes that have proven useful. The algorithms discussed in this book are concerned mainly with the compression of still-frame, continuous-tone, monochrome and color images, but some of the techniques, such as arithmetic coding, have found widespread use in the compression of bilevel images. Both lossless (bit-preserving) and lossy techniques are considered. A detailed description of the compression algorithm proposed as the world standard (the JPEG baseline algorithm) is provided. The book contains approximately 30 pages of reconstructed and error images illustrating the effect of each compression technique on a consistent image set, thus allowing for a direct comparison of bit rates and reconstucted image quality. For each algorithm, issues such as quality vs. bit rate, implementation complexity, and susceptibility to channel errors are considered.

Master's Thesis from the year 2014 in the subject Computer Science - Software, , course: Image Processing, language: English, abstract: In this thesis various methods for lossless compression of source image data are analyzed and discussed. The main focus in this work is lossless compression algorithms based on context modeling using tree structure. We are going to compare CALIC, GCT-I algorithms to the JPEG2000 standard algorithm which will be considered the reference of comparison. This work includes research on how to modify CALIC algorithm in continuous-tone mode by truncating tails of the error histogram which may lead to improve CALIC compression performance. Also, we are going to propose a modification to CALIC in binary mode by eliminating error feedback mechanism. As when any pixel to be encoded has a different grey level than any of the neighboring pixels, CALIC triggers an escape sequence that switches the algorithm from binary mode to continuous-tone mode. Which means in this case the pixel will be treated as if it was in continuous-tone region. This minor modification should improve CALIC performance in binary images. Finally, we are going to discuss the GCT-I on medical images and compare results to the JPEG2000 standard.

This book introduces advanced and hybrid compression techniques specifically used for medical images. The book discusses conventional compression and compressive sensing (CS) theory based approaches that are designed and implemented using various image transforms, such as: Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Singular Value Decomposition (SVD) and greedy based recovery algorithm. The authors show how these techniques provide simulation results of various compression techniques for different types of medical images, such as MRI, CT, US, and x-ray images. Future research directions are provided for medical imaging science. The book will be a welcomed reference for engineers, clinicians, and research students working with medical image compression in the biomedical imaging field. Covers various algorithms for data compression and medical image compression; Provides simulation results of compression algorithms for different types of medical images; Provides study of compressive sensing theory for compression of medical images.

Abstract: "We have classified four different images, under various levels of JPEG compression, using the following classification algorithms: minimum-distance, maximum-likelihood, and neural network. The training site accuracy and percent difference from the original classification were tabulated for each image compression level, with maximum-likelihood showing the poorest results. In general, as compression ratio increased, the classification retained its overall appearance, but much of the pixel-to-pixel detail was eliminated. We also examined the effect of compression on spatial pattern detection using a neural network."

What is Image Compression When applied to digital photographs, image compression is a form of data compression that helps to reduce the amount of money that is required for their storage or transmission. It is possible for algorithms to make use of visual perception and the statistical aspects of picture data in order to provide higher outcomes when compared to generic data compression approaches that are utilized for other types of digital data. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Image compression Chapter 2: Data compression Chapter 3: JPEG Chapter 4: Lossy compression Chapter 5: Lossless compression Chapter 6: PNG Chapter 7: Transform coding Chapter 8: Discrete cosine transform Chapter 9: JPEG 2000 Chapter 10: Compression artifact (II) Answering the public top questions about image compression. (III) Real world examples for the usage of image compression in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Image Compression.

The primary goal of the conference is to promote research and developmental activities in the recent trends in Computing, Communication and Electrical field It also helps to unite the great minds who believe in the need, for a technologically literate society This conference on Innovative Computing, Intelligent Communication and Smart Electrical systems promises enormous potential to stimulate educational opportunities to the researchers, academicians and post graduate students It is a powerful tool for the educators to interact with their peers, share knowledge and ideas It also offers innovative technical resources to support professional learning for educators and education leaders The two days conference comprises of keynote speeches and paper presentations in different sessions which helps in updating their knowledge thereby upgrading their organization and the overall Society