Surface compression with geometric bandelets
ACM SIGGRAPH 2005 Papers
An architecture for distributed wavelet analysis and processing in sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Adaptive distributed transforms for irregularly sampled Wireless Sensor Networks
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Sparse geometric image representations with bandelets
IEEE Transactions on Image Processing
Directionlets: anisotropic multidirectional representation with separable filtering
IEEE Transactions on Image Processing
Edge-preserving depth-map coding using graph-based wavelets
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Transform-based distributed data gathering
IEEE Transactions on Signal Processing
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In this work we consider the design of the lifting filters and trees used in a separable tree-based wavelet transform. We first consider the use of improved prediction filters, optimized to represent more efficiently smooth signals for arbitrary tree structures. We then consider the design of update filters that are orthogonal to neighboring prediction operators. While the corresponding decomposition is not fully orthogonal, near orthogonality between prediction and update operators leads to significant improvements in energy compaction. Finally we consider the design of trees that (i) avoid filtering across discontinuities in an image to reduce the amount of high frequency energy, while (ii) maintaining some regularity in the downsampled grids over multiple levels of decomposition in order to achieve good spatial localization of filtering.