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View-dependent information theory quality measures for pixel sampling and scene discretization in flatland

机译:基于视点的信息理论质量度量用于平地像素采样和场景离散化

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In this paper, we present view-dependent information theory quality measures for pixel sampling and scene discretization in flatland. The measures are based on a definition for the mutual information of a line, and have a purely geometrical basis. Several algorithms exploiting them are presented and compare well with an existing one based on depth differences.
机译:在本文中,我们提出了基于视角的信息理论质量度量,用于平地中的像素采样和场景离散化。这些度量基于对一条线的相互信息的定义,并且具有纯粹的几何基础。提出了几种利用它们的算法,并与基于深度差异的现有算法进行了很好的比较。

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