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Wavelets-based smoothness comparisons for volume data

机译:基于小波的体积数据平滑度比较

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摘要

In this study, the authors describe an objective smoothness assessment method for volume data. The metric can predict the extent of the difference in smoothness between a reference model, which may not be of perfect quality, and a distorted version. The proposed metric is based on the wavelet characterisation of Besov function spaces. The comparison of Besov norms between two models can resolve the global and local differences in smoothness between them. Experimental results from volume datasets with smoothing and sharpening operations demonstrate its effectiveness. By comparing direct volume rendered images, the experimental results show that the proposed smoothness index correlates well with human perceived vision. Finally, the metric can help the analyse compression distortions when they compare volume data with different smoothness.
机译:在这项研究中,作者描述了一种针对体积数据的客观平滑度评估方法。该度量可以预测参考模型(可能不是完美质量)与变形版本之间的平滑度差异程度。提出的度量基于Besov函数空间的小波表征。两个模型之间的Besov规范的比较可以解决它们之间平滑度的全局和局部差异。来自体积数据集的平滑和锐化操作的实验结果证明了其有效性。通过比较直接渲染的体积图像,实验结果表明,提出的平滑度指数与人的感知视觉具有很好的相关性。最后,该度量标准在比较具有不同平滑度的体数据时可以帮助分析压缩失真。

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