18,150 research outputs found
Segmentwise Discrete Wavelet Transform
DizertaÄŤnĂ práce se zabĂ˝vá algoritmy SegDWT pro segmentovĂ˝ vĂ˝poÄŤet DiskrĂ©tnĂ WaveletovĂ© Transformace – DWT jedno i vĂcedimenzionálnĂch dat. SegmentovĂ˝m vĂ˝poÄŤtem se rozumĂ zpĹŻsob vĂ˝poÄŤtu waveletovĂ© analĂ˝zy a syntĂ©zy po nezávislĂ˝ch segmentech (blocĂch) s urÄŤitĂ˝m pĹ™ekryvem tak, Ĺľe nevznikajĂ blokovĂ© artefakty. AnalyzujĂcà část algoritmu pracuje na principu odstranÄ›nĂ pĹ™esahu a produkuje vĹľdy část waveletovĂ˝ch koeficientĹŻ z waveletovĂ© transformace celĂ©ho signálu, kterĂ© mohou bĂ˝t následnÄ› libovolnÄ› zpracovány a podrobeny zpÄ›tnĂ© transformaci. RekonstruovanĂ© segmenty jsou pak skládány podle principu pĹ™iÄŤtenĂ pĹ™esahu. Algoritmus SegDWT, ze kterĂ©ho tato práce vycházĂ, nenĂ v souÄŤasnĂ© podobnÄ› pĹ™Ămo pouĹľitelnĂ˝ pro vĂcerozmÄ›rnĂ© signály. Tato práce obsahuje nÄ›kolik jeho modifikacĂ a následnĂ© zobecnÄ›nĂ pro vĂcerozmÄ›rnĂ© signály pomocĂ principu separability. KromÄ› toho je v práci pĹ™edstaven algoritmus SegLWT, kterĂ˝ myšlenku SegDWT pĹ™enášà na vĂ˝poÄŤet waveletovĂ© transformace pomocĂ nekauzálnĂch struktur filtrĹŻ typu lifting.The dissertation deals with SegDWT algorithms performing a segmented (segmentwise) computation of one- and multi-dimensional Discrete Wavelet Transform – DWT. The segmented approach allows one to perform the segment (block) wavelet analysis and synthesis using segment overlaps while preventing blocking artifacts. The parts of the wavelet coefficients of the whole signal wavelet transform corresponding to the actual segment are produced by the analysis part of the algorithm exploiting overlap-save principle. The resulting coefficients belonging to the segment can be processed arbitrarily and than they can transformed back to the original domain. The reconstructed segments are than put together using overlap add principle. The already known SegDWT algorithm can not be effectively used on multidimensional signals. Several modifications of the algorithm are proposed which makes it possible to generalize it to multidimensional cases using separability property. In addition, the thesis presents SegLWT algorithm adopting ideas of the SegDWT and transferring it to the non-causal lifting filter bank structures.
The Parallel Algorithm for the 2-D Discrete Wavelet Transform
The discrete wavelet transform can be found at the heart of many
image-processing algorithms. Until now, the transform on general-purpose
processors (CPUs) was mostly computed using a separable lifting scheme. As the
lifting scheme consists of a small number of operations, it is preferred for
processing using single-core CPUs. However, considering a parallel processing
using multi-core processors, this scheme is inappropriate due to a large number
of steps. On such architectures, the number of steps corresponds to the number
of points that represent the exchange of data. Consequently, these points often
form a performance bottleneck. Our approach appropriately rearranges
calculations inside the transform, and thereby reduces the number of steps. In
other words, we propose a new scheme that is friendly to parallel environments.
When evaluating on multi-core CPUs, we consistently overcome the original
lifting scheme. The evaluation was performed on 61-core Intel Xeon Phi and
8-core Intel Xeon processors.Comment: accepted for publication at ICGIP 201
Simple Signal Extension Method for Discrete Wavelet Transform
Discrete wavelet transform of finite-length signals must necessarily handle
the signal boundaries. The state-of-the-art approaches treat such boundaries in
a complicated and inflexible way, using special prolog or epilog phases. This
holds true in particular for images decomposed into a number of scales,
exemplary in JPEG 2000 coding system. In this paper, the state-of-the-art
approaches are extended to perform the treatment using a compact streaming
core, possibly in multi-scale fashion. We present the core focused on CDF 5/3
wavelet and the symmetric border extension method, both employed in the JPEG
2000. As a result of our work, every input sample is visited only once, while
the results are produced immediately, i.e. without buffering.Comment: preprint; presented on ICSIP 201
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