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Statements

Subject Item
dbr:Multirate_filter_bank_and_multidimensional_directional_filter_banks
dbo:wikiPageWikiLink
dbr:Multidimensional_Multirate_Systems
Subject Item
dbr:Multidimensional_Multirate_Systems
rdfs:label
Multidimensional Multirate Systems
rdfs:comment
Multidimensional Multirate systems find applications in image compression and coding. Several applications such as conversion between progressive video signals require usage of multidimensional multirate systems. In multidimensional multirate systems, the basic building blocks are decimation matrix (M), expansion matrix(L) and Multidimensional digital filters. The decimation and expansion matrices have dimension of D x D, where D represents the dimension. To extend the one dimensional (1-D) multirate results, there are two different ways which are based on the structure of decimation and expansion matrices. If these matrices are diagonal, separable approaches can be used, which are separable operations in each dimension. Although separable approaches might serve less complexity, non-separa
foaf:depiction
n11:Figure5giff.gif n11:Maximally_decimated_filter.gif
dcterms:subject
dbc:Video_signal dbc:Digital_signal_processing
dbo:wikiPageID
44393430
dbo:wikiPageRevisionID
995248424
dbo:wikiPageWikiLink
n14:Maximally_decimated_filter.GIF dbr:Decimation_(signal_processing) n14:Figure5giff.GIF dbr:Interpolation_(computer_graphics) dbc:Digital_signal_processing dbc:Video_signal dbr:Image_compression
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dbo:abstract
Multidimensional Multirate systems find applications in image compression and coding. Several applications such as conversion between progressive video signals require usage of multidimensional multirate systems. In multidimensional multirate systems, the basic building blocks are decimation matrix (M), expansion matrix(L) and Multidimensional digital filters. The decimation and expansion matrices have dimension of D x D, where D represents the dimension. To extend the one dimensional (1-D) multirate results, there are two different ways which are based on the structure of decimation and expansion matrices. If these matrices are diagonal, separable approaches can be used, which are separable operations in each dimension. Although separable approaches might serve less complexity, non-separable methods, with non-diagonal expansion and decimation matrices, provide much better performance. The difficult part in non-separable methods is to create results in MD case by extend the 1-D case. Polyphase decomposition and maximally decimated reconstruction systems are already carried out. MD decimation / interpolation filters derived from 1-D filters and maximally decimated filter banks are widely used and constitute important steps in the design of multidimensional multirate systems.
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wikipedia-en:Multidimensional_Multirate_Systems
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dbr:Multidimensional_Multirate_Systems