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Title: Discrete wavelet transform based Hybrid image coding schemes for continuous tone images
Researcher: Venkateswaran, N
Guide(s): Ramana Rao, Y V
Keywords: Absolute Moment Block Truncation
Cellular Neural Networks
Decomposition and Clustering
Discrete Wavelet Transform
Upload Date: 31-Oct-2014
University: Anna University
Completed Date: 01/12/2007
Abstract: The thesis aims to propose new hybrid algorithms for wavelet newlinebased image compression of continuous tone still images to achieve low bit newlinerates The algorithms have been proposed by combining Discrete Wavelet newlineTransform Absolute Moment Block Truncation Coding Singular Value newlineDecomposition and Clustering of data for the purpose of vector quantization newlineThe proposed algorithms offer improved compression ratio The proposed newlinemethods utilize the property of wavelet transform in which most of the detail newlinesubband coefficients remain quantized to zero Also a set of Daubechies newlinewavelet templates are designed to be used with Cellular Neural Networks in newlinehigh speed image compression applications newlineIn this thesis two hybrid image compression algorithms that are newlinebased on Absolute Moment Block Truncation Coder AMBTC and DWT are newlineproposed The proposed method is very attractive due to its reduced newlinecomputational complexity newline newline
Pagination: xviii, 124p.
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File40.22 kBAdobe PDFView/Open
02_certificate.pdf7.3 kBAdobe PDFView/Open
03_abstract.pdf9.05 kBAdobe PDFView/Open
04_acknowledgement.pdf6.55 kBAdobe PDFView/Open
05_content.pdf46.51 kBAdobe PDFView/Open
06_chapter1.pdf121.1 kBAdobe PDFView/Open
07_chapter2.pdf635.17 kBAdobe PDFView/Open
08_chapter3.pdf3.23 MBAdobe PDFView/Open
09_chapter4.pdf5.57 MBAdobe PDFView/Open
10_chapter5.pdf4.68 MBAdobe PDFView/Open
11_chapter6.pdf23.78 kBAdobe PDFView/Open
12_reference.pdf37.39 kBAdobe PDFView/Open
13_publication.pdf9.16 kBAdobe PDFView/Open
14_vitae.pdf5.66 kBAdobe PDFView/Open

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