Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/18784
Title: Vector quantization based efficient still image compression algorithms
Researcher: Mary Shanthi Rani M
Guide(s): Somasundaram K
Keywords: Computer Science
Upload Date: 29-May-2014
University: Mother Teresa Womens University
Completed Date: 19/11/2012
Abstract: The rapid growth and development of electronic imaging in the recent years has led to large scale digital media archives. These are increasingly becoming popular as more and more digital media contents are created and deployed online every day. A critical issue in designing such archives is effective storage of the data. Uncompressed data requires more storage and huge bandwidth for transmission. Though the cost of storage is rapidly dropping, compression still remains as a challenging issue due to the growing number of multimedia based online applications. This necessitates the design of highly efficient image compression systems which promise good image quality and compression ratios with low computational complexity. In this thesis, six methods have been developed for still image compression based on Vector Quantization (VQ). Our first method A Pattern Based Residual Vector Quantization (PBRVQ) is a two-stage residual vector quantization algorithm that employs an innovative approach to VQ, using number patterns as the codebook. The novelty of this method is the use of high eigen-valued blocks as initial seeds which serve as good distributors in the formation of clusters and fast convergence. The number patterns, which form the secondary codebook are easily generated without complex calculations by applying basic ideas from combinatorics. A Quality Control Parameter (QCP) has been used to tune the picture quality and bit rate as well. This method offers several advantages: 1) the computational complexity is greatly reduced; 2) exhaustive comparisons in VQ are carried out more efficiently; 3) the picture quality of the reconstructed image is not compromised; and 4) a reduced bit rate is achieved. The performance of this method is superior to similar vector quantization techniques in terms of picture quality, computational complexity and bit rate.
Pagination: 151p.
URI: http://hdl.handle.net/10603/18784
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File18.93 kBAdobe PDFView/Open
02_abstract.pdf22.88 kBAdobe PDFView/Open
03_declaration.pdf7.57 kBAdobe PDFView/Open
04_acknowledgement.pdf11.43 kBAdobe PDFView/Open
05_contents.pdf11.72 kBAdobe PDFView/Open
06_list_of_tables.pdf15.13 kBAdobe PDFView/Open
07_list_of_figures.pdf21.51 kBAdobe PDFView/Open
08_abbreviations.pdf81.04 kBAdobe PDFView/Open
09_chapter 1.pdf2.05 MBAdobe PDFView/Open
10_chapter 2.pdf323.65 kBAdobe PDFView/Open
11_chapter 3.pdf757.72 kBAdobe PDFView/Open
12_chapter 4.pdf1.78 MBAdobe PDFView/Open
13_chapter 5.pdf1.59 MBAdobe PDFView/Open
14_chapter 6.pdf1.82 MBAdobe PDFView/Open
15_chapter 7.pdf2.3 MBAdobe PDFView/Open
16_chapter 8.pdf30.31 kBAdobe PDFView/Open
17_bibliography.pdf72.49 kBAdobe PDFView/Open


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