title: Hierarchical tensor approximation of multi-dimensional visual data.
abstract: Visual data comprise of multi-scale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional dataset is transformed into a hierarchy of signals to expose its multi-scale structures. The signal at each level of the hierarchy is further divided into a number of smaller tensors to expose its spatially inhomogeneous structures. These smaller tensors are further transformed and pruned using a tensor approximation technique. Our hierarchical tensor approximation supports progressive transmission and partial decompression. Experimental results indicate that our technique can achieve higher compression ratios and quality than previous methods, including wavelet transforms, wavelet packet transforms, and single-level tensor approximation. We have successfully applied our technique to multiple tasks involving multi-dimensional visual data, including medical and scientific data visualization, data-driven rendering and texture synthesis.
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. qingwu1@uiuc.edu
authors.forename: Qing
authors.initials: Q
authors.lastname: Wu
authors.forename: Tian
authors.initials: T
authors.lastname: Xia
authors.forename: Chun
authors.initials: C
authors.lastname: Chen
authors.forename: Hsueh-Yi Sean
authors.initials: HY
authors.lastname: Lin
authors.forename: Hongcheng
authors.initials: H
authors.lastname: Wang
authors.forename: Yizhou
authors.initials: Y
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology; D003196:Computer Graphics; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity