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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00211050%21RIV14-MSM-21230___
rdf:type
skos:Concept n18:Vysledek
rdfs:seeAlso
http://www.cescg.org/CESCG-2013/papers/Egert-Efficient_GPU-based_Decompression_of_BTF_Data_Compressed_using_Multi-Level_Vector_Quantization.pdf
dcterms:description
One of the main drawbacks of Bidirectional Texture Function (BTF), as a method of capturing realistic and accurate real-world material appearance, is the resulting size of the measured data set. Several lossy methods to compress the data were proposed over the years to cope with this problem. To efficiently use the compressed data an appropriate decompression algorithms are also needed, allowing fast random synthesis of BTF data without the need to reconstruct the whole BTF back to its original representation. One of such methods based on multi-level vector quantization and providing both good compression ratio and random access from the compressed data was proposed by Havran et al. in 2010. In this paper, we would like to share our experience with writing a GPU based implementation of the decompression part of the aforementioned method. Our goal was to evaluate the implementation difficulty, as well as the resulting performance and suitability of the algorithm for real-time use. One of the main drawbacks of Bidirectional Texture Function (BTF), as a method of capturing realistic and accurate real-world material appearance, is the resulting size of the measured data set. Several lossy methods to compress the data were proposed over the years to cope with this problem. To efficiently use the compressed data an appropriate decompression algorithms are also needed, allowing fast random synthesis of BTF data without the need to reconstruct the whole BTF back to its original representation. One of such methods based on multi-level vector quantization and providing both good compression ratio and random access from the compressed data was proposed by Havran et al. in 2010. In this paper, we would like to share our experience with writing a GPU based implementation of the decompression part of the aforementioned method. Our goal was to evaluate the implementation difficulty, as well as the resulting performance and suitability of the algorithm for real-time use.
dcterms:title
Efficient GPU-based Decompression of BTF Data Compressed using Multi-Level Vector Quantization Efficient GPU-based Decompression of BTF Data Compressed using Multi-Level Vector Quantization
skos:prefLabel
Efficient GPU-based Decompression of BTF Data Compressed using Multi-Level Vector Quantization Efficient GPU-based Decompression of BTF Data Compressed using Multi-Level Vector Quantization
skos:notation
RIV/68407700:21230/13:00211050!RIV14-MSM-21230___
n18:predkladatel
n19:orjk%3A21230
n3:aktivita
n23:S n23:P
n3:aktivity
P(GAP202/12/2413), S
n3:dodaniDat
n10:2014
n3:domaciTvurceVysledku
n5:4980654
n3:druhVysledku
n17:D
n3:duvernostUdaju
n7:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
72067
n3:idVysledku
RIV/68407700:21230/13:00211050
n3:jazykVysledku
n15:eng
n3:klicovaSlova
BTF; bidirectional texture function; decompression; GPU; OpenCL; Multi-level vector quantization
n3:klicoveSlovo
n12:Multi-level%20vector%20quantization n12:GPU n12:decompression n12:bidirectional%20texture%20function n12:OpenCL n12:BTF
n3:kontrolniKodProRIV
[35ED7F017F74]
n3:mistoKonaniAkce
Smolenice
n3:mistoVydani
Vienna
n3:nazevZdroje
Proceedings of the 17th Central European Seminar on Computer Graphics
n3:obor
n4:JC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n22:GAP202%2F12%2F2413
n3:rokUplatneniVysledku
n10:2013
n3:tvurceVysledku
Egert, Petr
n3:typAkce
n20:WRD
n3:zahajeniAkce
2013-04-28+02:00
s:numberOfPages
8
n21:hasPublisher
Institute of Computer Graphics and Algorithms
n6:isbn
978-3-9502533-5-1
n8:organizacniJednotka
21230