{"title":"Product integral binding coefficients for high-order wavelets","authors":"Nick Michiels, J. Put, P. Bekaert","doi":"10.5220/0005013300170024","DOIUrl":null,"url":null,"abstract":"This paper provides an efficient algorithm to calculate product integral binding coefficients for a heterogeneous mix of wavelet bases. These product integrals are ubiquitous in multiple applications such as signal processing and rendering. Previous work has focused on simple Haar wavelets. Haar wavelets excel at encoding piecewise constant signals, but are inadequate for compactly representing smooth signals for which high-order wavelets are ideal. Our algorithm provides an efficient way to calculate the tensor of these binding coefficients. The algorithm exploits both the hierarchical nature and vanishing moments of the wavelet bases, as well as the sparsity and symmetry of the tensor. We demonstrate the effectiveness of high-order wavelets with a rendering application. The smoother wavelets represent the signals more effectively and with less blockiness than the Haar wavelets of previous techniques.","PeriodicalId":438702,"journal":{"name":"2014 International Conference on Signal Processing and Multimedia Applications (SIGMAP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Signal Processing and Multimedia Applications (SIGMAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0005013300170024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper provides an efficient algorithm to calculate product integral binding coefficients for a heterogeneous mix of wavelet bases. These product integrals are ubiquitous in multiple applications such as signal processing and rendering. Previous work has focused on simple Haar wavelets. Haar wavelets excel at encoding piecewise constant signals, but are inadequate for compactly representing smooth signals for which high-order wavelets are ideal. Our algorithm provides an efficient way to calculate the tensor of these binding coefficients. The algorithm exploits both the hierarchical nature and vanishing moments of the wavelet bases, as well as the sparsity and symmetry of the tensor. We demonstrate the effectiveness of high-order wavelets with a rendering application. The smoother wavelets represent the signals more effectively and with less blockiness than the Haar wavelets of previous techniques.