| Paper: | TP-L2.5 |
| Session: | Image and Video Filtering and Multiresolution Processing |
| Time: | Tuesday, September 18, 16:10 - 16:30 |
| Presentation: |
Lecture
|
| Title: |
HIERARCHICAL TENSOR APPROXIMATION OF MULTIDIMENSIONAL IMAGES |
| Authors: |
Qing Wu; University of Illinois at Urbana-Champaign | | |
| | Tian Xia; University of Illinois at Urbana-Champaign | | |
| | Yizhou Yu; University of Illinois at Urbana-Champaign | | |
| Abstract: |
Visual data comprises of multi-scale and inhomogeneous signals. In this paper, we exploit these characteristics and develop an adaptive data approximation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional image 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 collective tensor approximation technique. Experimental results indicate that our technique can achieve higher compression ratios than existing functional approximation methods, including wavelet transforms, wavelet packet transforms and single-level tensor approximation. |
