Feature-Based Texture Synthesis and Hierarchical Tensor Approximation

Finally, we propose to apply multilinear models to wavelet domain to reduce overhead. High-frequency wavelet sub-bands are subdivided into small blocks most of which get pruned. The blocks are usually correlated especially when properly classified. Different channels and sub-bands may exhibit strong redundancy as well. We reorganize the subdivided blocks into small tensors, classify the unpruned ones and approximate each cluster as a tensor ensemble. Experiments on images and medical volume data indicate that this approach achieves better approximation quality than wavelet (packet) transforms and hybrid linear models.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Wu, Qing

Contributors dc:contributor

• Yu, Yizhou

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3301250

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81806