Multihypothesis motion compensation is extended into the transform domain by using a redundant wavelet transform to produce multiple predictions that are diverse in transform phase. The corresponding inverse transform implicitly combines the multihypothesis predictions into a single spatial-domain prediction for motion compensation such that no side information is needed to describe the combination weights. Additionally, we use a hierarchical search to tailor the motion-vector field to individual phases. Substantial gains in rate-distortion performance are obtained in comparison to an equivalent system using single-phase prediction.
A video coder is presented that combines mesh-based motion-compensated temporal filtering, phase-diversity multihypothesis motion compensation, and an embedded 3D wavelet-coefficient coder. The key contribution of this work is the introduction of the phasediversity multihypothesis paradigm into motion-compensated temporal filtering, which is achieved by deploying temporal filtering in the domain of a spatially redundant wavelet transform. A regular triangle mesh is used to track motion between frames, and an affine transform between mesh triangles implements motion compensation within a lifting-based temporal transform. Experimental results reveal that the incorporation of phase-diversity multihypothesis into mesh-based motion-compensated temporal filtering significantly improves the rate-distortion performance of the 3D video coder.
Many applications in a variety of scientific domains produce datasets that consist of a data field lying on a sampling grid that may not be uniformly spaced. However, progressive access for visualization, exploration, and communication of these datasets is a critical issue, and wavelet-based embedded coding an attractive solution given its prior success in realms such as image coding. As grid information may compose a significant portion, or even a majority, of that of the overall dataset, coding the grid as initial overhead is often impractical. Approaches for the joint embedded coding of data and grid are proposed using first-generation wavelet transforms so as not to require prior grid knowledge for transform inversion. In one proposed technique, two independently generated embedded codings, one for data and one for grid, are interleaved. As an alternative, an embedded vector-valued coder, in which data and grid are combined into a single vector-valued field, is considered. Experimental results are reported that favor the former approach over the latter.
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Conclusion: Deep learning with multiple harmonized data sources can yield effective models for OPC primary and nodal segmentation using CT alone. The utility of these models will depend on the clinical use case and will be explored on further investigation, though current model performance metrics, particularly for nodal segmentation, are likely adequate for prospective testing in clinical and research applications.
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