Conference paper

BAŘINA David and ZEMČÍK Pavel. Minimum Memory Vectorisation of Wavelet Lifting. In: Advanced Concepts for Intelligent Vision Systems (ACIVS). Poznan: Springer London, 2013, pp. 91-101. ISBN 978-3-319-02894-1.
Publication language:english
Original title:Minimum Memory Vectorisation of Wavelet Lifting
Title (cs):Vektorizace vlnkové transformace
Pages:91-101
Proceedings:Advanced Concepts for Intelligent Vision Systems (ACIVS)
Conference:Advanced Concepts for Intelligent Vision Systems (ACIVS) 2013
Series:Lecture Notes in Computer Science (LNCS) 8192
Place:Poznan, PL
Year:2013
ISBN:978-3-319-02894-1
Publisher:Springer London
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Keywords
discrete wavelet transform, lifting scheme, parallelization, vectorisation, SIMD
Annotation
The subject of this paper is to introduce novel vectorisation of discrete wavelet transform implementation using lifting scheme.
Abstract
With the start of the widespread use of discrete wavelet transform the need for its effective implementation is becoming increasingly more important. This work presents a novel approach to discrete wavelet transform through a new computational scheme of wavelet lifting. The presented approach is compared with two other. The results are obtained on a general purpose processor with 4-fold SIMD instruction set (such as Intel x86-64 processors). Using the frequently exploited CDF 9/7 wavelet, the achieved speedup is about 3× compared to naive implementation.
BibTeX:
@INPROCEEDINGS{
   author = {David Ba{\v{r}}ina and Pavel Zem{\v{c}}{\'{i}}k},
   title = {Minimum Memory Vectorisation of Wavelet Lifting},
   pages = {91--101},
   booktitle = {Advanced Concepts for Intelligent Vision Systems (ACIVS)},
   series = {Lecture Notes in Computer Science (LNCS) 8192},
   year = {2013},
   location = {Poznan, PL},
   publisher = {Springer London},
   ISBN = {978-3-319-02894-1},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=10420}
}

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