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Other titles in the Springer Briefs in Computer Science series:

Lossy Image Compression: Domain Decomposition-Based Algorithms (Springerbriefs in Computer Science)

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Lossy Image Compression: Domain Decomposition-Based Algorithms (Springerbriefs in Computer Science) Cover

 

Synopses & Reviews

Publisher Comments:

Good quality digital images have high storage and bandwidth requirements. In modern times, with increasing user expectation for image quality, efficient compression is necessary to keep memory and transmission time within reasonable limits. 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 algorithmsDetailed discussion of fuzzy geometry measures and their application in image compression algorithmsNew domain decomposition based algorithms using image quality measures and study of various quality measures for gray scale image compressionCompression algorithms for different parallel architectures and evaluation of time complexity for encoding on all architecturesParallel implementation of image compression algorithms on a cluster in Parallel Virtual Machine (PVM) environment.This book will be of interest to graduate students, researchers and practicing engineers looking for new image compression techniques that provide good perceived quality in digital images with higher compression ratios than is possible with conventional algorithms.

Synopsis:

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.

Table of Contents

Introduction Tree Triangular Coding Image Compression Algorithms Image Compression Using Quality Measures Parallel Image Compression Algorithms Conclusions and Future Directions

Product Details

ISBN:
9781447122173
Author:
Shukla, K. K.
Publisher:
Springer
Author:
Prasad, M.V.
Subject:
Computer Graphics - General
Subject:
Compression Ratio
Subject:
Domain Decompression
Subject:
Fuzzy measures
Subject:
Image compression.
Subject:
Parallel algorithms.
Subject:
parallel virtual machine
Subject:
Speedup
Subject:
Image Processing and Computer Vision
Subject:
Graphics-General
Copyright:
Edition Description:
2011
Series:
SpringerBriefs in Computer Science
Publication Date:
20111028
Binding:
TRADE PAPER
Language:
English
Pages:
101
Dimensions:
235 x 155 mm

Related Subjects

Computers and Internet » Graphics » General
Reference » Science Reference » Technology
Science and Mathematics » Electricity » General Electronics

Lossy Image Compression: Domain Decomposition-Based Algorithms (Springerbriefs in Computer Science) New Trade Paper
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Product details 101 pages Springer - English 9781447122173 Reviews:
"Synopsis" by , 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.
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