Fractal Image Encoding Using Fringe Based Property - Couverture souple

Aggarwal, Indu

 
9783659919398: Fractal Image Encoding Using Fringe Based Property
Couverture souple
ISBN 10 : 365991939X ISBN 13 : 9783659919398
Editeur : LAP LAMBERT Academic Publishing, 2016

Synopsis

Fractal image encoding is one of the famous lossy encoding techniques ascertain high compression ratio, higher PSNR and good quality of encoded image. It is the approach that uses self similarity property in natural image.The main drawback of fractal image encoding is time consumption in search of appropriate domain for each range of image blocks.There have been various researches carried out to overcome the limitation of fractal encoding and to speed up the encoder.The neighborhood search strategy used in spatial domain reduces the encoding time from linear time to logarithmic time.The proposed algorithm used k-nearest neighbor search to find the similar edge shaped range and domain blocks to be mapped on the basis of DCT lowest coefficients in horizontal and vertical directions and then using similarity measure; the virtual codebook was prepared to encode the image and then decoded the image using averaging of pixels of selected domains. The proposed approach explained in this book is compared with the existing method and this work gave high PSNR value, high compression ratio and reduced MSE computations with little decay in image quality which is acceptable.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Fractal image encoding is one of the famous lossy encoding techniques ascertain high compression ratio, higher PSNR and good quality of encoded image. It is the approach that uses self similarity property in natural image.The main drawback of fractal image encoding is time consumption in search of appropriate domain for each range of image blocks.There have been various researches carried out to overcome the limitation of fractal encoding and to speed up the encoder.The neighborhood search strategy used in spatial domain reduces the encoding time from linear time to logarithmic time.The proposed algorithm used k-nearest neighbor search to find the similar edge shaped range and domain blocks to be mapped on the basis of DCT lowest coefficients in horizontal and vertical directions and then using similarity measure; the virtual codebook was prepared to encode the image and then decoded the image using averaging of pixels of selected domains. The proposed approach explained in this book is compared with the existing method and this work gave high PSNR value, high compression ratio and reduced MSE computations with little decay in image quality which is acceptable.

Biographie de l'auteur

Indu Aggarwal has completed her M.Tech in Computer Science and Engineering from Amity University, Noida in 2015.She completed her research work in the area of Image Processing and presented various research papers in International Conferences. Her interest and passion of research encouraged her to write and to help people working in this area.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2016
Langue
anglais
ISBN 10 
365991939X
ISBN 13 
9783659919398
Reliure
Broché
Nombre de pages
72
Coordonnées du fabricant
non disponible
Personne responsable
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