Conditional entropy-constrained vector quantization of chromatic information in document images.

dc.contributor.advisor	Dubois, E.,
dc.contributor.author	Guillope, Vincent.
dc.date.accessioned	2009-03-23T13:01:17Z
dc.date.available	2009-03-23T13:01:17Z
dc.date.created	2002
dc.date.issued	2002
dc.degree.level	Masters
dc.degree.name	M.A.Sc.
dc.description.abstract	We present in this thesis original research on Conditional Entropy-Constrained Vector Quantization (CECVQ) of chrominance information in document images. First, we review the existing image compression formats that could be used for document images: JPEG 2000, DjVu, LDF and PNG. Then, we develop the CECVQ algorithm to adapt it to image quantization and we show its excellent distortion-rate performance. Finally, we develop our own coding scheme targeted at the compression of chrominance information and based on the piecewise-constant model. Color space transformation, CECVQ and context-based entropy coding form the skeleton of our scheme. We also show the strengths and the weaknesses of this new coding scheme, and suggest possible improvements and other approaches.
dc.format.extent	74 p.
dc.identifier.citation	Source: Masters Abstracts International, Volume: 41-05, page: 1487.
dc.identifier.isbn	9780612765825
dc.identifier.uri	http://hdl.handle.net/10393/6122
dc.identifier.uri	http://dx.doi.org/10.20381/ruor-14693
dc.publisher	University of Ottawa (Canada)
dc.subject.classification	Engineering, Electronics and Electrical.
dc.title	Conditional entropy-constrained vector quantization of chromatic information in document images.
dc.type	Thesis

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