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En-route data compression is fundamental to reduce the power consumed for data gathering in sensor networks. Typical in-network compression schemes involve the distributed computation of some decorrelating transform on the data; the structure along which the transform is computed influences both coding performance and transmission cost of the computed coefficients, and has been widely explored in...
We design lifting-based wavelet transforms for any arbitrary communication graph in a wireless sensor network (WSN). Since transmitting raw data bits along the routing trees in WSN usually requires more bits than transmitting encoded data, we seek to minimize raw data transmissions in the network. We especially focus on unidirectional transforms which are computed as data is forwarded towards the...
A general class of unidirectional transforms is presented that can be computed in a distributed manner along an arbitrary routing tree. Additionally, we provide a set of conditions under which these transforms are invertible. These transforms can be computed as data is routed towards the collection (or sink) node in the tree and exploit data correlation between nodes in the tree. Moreover, when used...
In order to efficiently encode depth map images in a multi-view video coding scenario, two basic properties of these images can be leveraged: first, errors in pixels located near the edges of objects have a greater perceptual impact on the synthesized view; second, depth maps can be approximated as piecewise planar signals. We make use of these facts to define a discrete wavelet transform using lifting...
We address the design and optimization of an energy-efficient lifting-based 2D transform for wireless sensor networks with irregular spatial sampling. The 2D transform is designed to allow for unidirectional computation found in existing path-wise transforms, thereby eliminating costly backward transmissions often required by existing 2D transforms, while simultaneously achieving greater data decorrelation...
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