公开(公告)号：US08085888B2

公开(公告)日：2011-12-27

申请号：US11580453

申请日：2006-10-13

申请人： Erik Ordentlich ,  Marcelo J. Weinberger ,  Itschak Weissman ,  Gadiel Seroussi

发明人： Erik Ordentlich ,  Marcelo J. Weinberger ,  Itschak Weissman ,  Gadiel Seroussi

IPC分类号： H03D1/04

CPC分类号： G06T5/002 ,  G06T2207/20076

摘要： In various embodiments of the present invention, optimal or near-optimal multidirectional context sets for a particular data-and/or-signal analysis or processing task are determined by selecting a maximum context size, generating a set of leaf nodes corresponding to those maximally sized contexts that occur in the data or signal to be processed or analyzed, and then building up and concurrently pruning, level by level, a multidirectional optimal context tree constructing one of potentially many optimal or near-optimal context trees in which leaf nodes represent the context of a near-optimal or optimal context set that may contain contexts of different sizes and geometries. Pruning is carried out using a problem-domain-related weighting function applicable to nodes and subtrees within the context tree. In one described embodiment, a bi-directional context tree suitable for a signal denoising application is constructed using, as the weighting function, an estimated loss function.

摘要翻译： 在本发明的各种实施例中，通过选择最大上下文大小来确定用于特定数据和/或信号分析或处理任务的最佳或接近最佳的多向上下文集合，生成对应于那些最大尺寸 发生在待处理或分析的数据或信号中的上下文，然后逐级建立和并行修剪，构建叶节点代表上下文的潜在许多最优或近最优上下文树之一的多向最佳上下文树 可以包含不同尺寸和几何形状的上下文的近似或最佳上下文集合。 使用适用于上下文树中的节点和子树的问题域相关加权函数来执行修剪。 在一个描述的实施例中，使用适用于信号去噪应用的双向上下文树，使用估计的损耗函数作为加权函数。

公开(公告)号：US07123172B1

公开(公告)日：2006-10-17

申请号：US11192559

申请日：2005-07-29

申请人： Erik Ordentlich ,  Marcelo J. Weinberger ,  Itschak Weissman ,  Gadiel Seroussi

发明人： Erik Ordentlich ,  Marcelo J. Weinberger ,  Itschak Weissman ,  Gadiel Seroussi

IPC分类号： H03M7/34 ,  H03M7/38

CPC分类号： G06T5/002 ,  G06T2207/20076

摘要： In various embodiments of the present invention, optimal or near-optimal multidirectional context sets for a particular data-and/or-signal analysis or processing task are determined by selecting a maximum context size, generating a set of leaf nodes corresponding to those maximally sized contexts that occur in the data or signal to be processed or analyzed, and then building up and concurrently pruning, level by level, a multidirectional optimal context tree constructing one of potentially many optimal or near-optimal context trees in which leaf nodes represent the context of a near-optimal or optimal context set that may contain contexts of different sizes and geometries. Pruning is carried out using a problem-domain-related weighting function applicable to nodes and subtrees within the context tree. In one described embodiment, a bi-directional context tree suitable for a signal denoising application is constructed using, as the weighting function, an estimated loss function.

摘要翻译： 在本发明的各种实施例中，通过选择最大上下文大小来确定用于特定数据和/或信号分析或处理任务的最佳或接近最佳的多向上下文集合，生成对应于那些最大尺寸 发生在待处理或分析的数据或信号中的上下文，然后逐级建立和并行修剪，构建叶节点代表上下文的潜在许多最优或近最优上下文树之一的多向最佳上下文树 可以包含不同尺寸和几何形状的上下文的近似或最佳上下文集合。 使用适用于上下文树中的节点和子树的问题域相关加权函数来执行修剪。 在一个描述的实施例中，使用适用于信号去噪应用的双向上下文树，使用估计的损耗函数作为加权函数。