logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Tensor product multiresolution analysis with error control for compact image representation

delete2002-04-01
delete61
PRE
AI
S
Sergio Amat *
F
Francesc Aràndiga
A
Albert Cohen
R
Rosa Donat
DOI：10.1016/S0165-1684(01)00206-7delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
A class of multiresolution representations based on nonlinear prediction is studied in the multivatiate context based on tensor product strategies. In contrast to standard linear wavelet transforms, these representations cannot be thought of as a change of basis, and the error induced by thresholding or quantizing the coefficients requires a different analysis. We propose specific error control algorithms which ensure a prescribed accuracy in various norms when performing such operations on the coefficients. These algorithms are compared with standard thresholding, for synthetic and real images. (C) 2002 Elsevier Science B.V. All rights reserved.
Keywords:
tensor product
multi-scale decomposition
non linearity
stability
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Signal Processing cover
Signal Processing
IF:
3.6
Papers:
9.9K
Citations:
1.7W

Organization

No organization information available