Könyv Lossy Image Compression K.K. Shukla

Lossy Image Compression

Domain Decomposition-Based Algorithms

Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 5-8 napon belül
18 253 Ft
Good quality digital images have high storage and bandwidth requirements. In modern times, with incr...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2011
oldal
89
EAN
9781447122173
ISBN
1447122178
Enbook ID
01425671
Súly
172
Méretek
155 x 235 x 7

Teljes leírás

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 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.§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.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.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.

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