Ieee Transactions on Visualization and Computer Graphics 1 a Statistical Approach to Volume Data Quality Assessment
Ieee Transactions on Visualization and Computer Graphics 1 a Statistical Approach to Volume Data Quality Assessment
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通讯作者：
Chaoli Wang;K. Ma
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作者：
Chaoli Wang;K. Ma
- 质量评估在数据分析中起着至关重要的作用。在本文中，我们提出了一个减少参考的方法来评估体积数据的质量。我们的算法提取重要的统计信息，从原始数据中的小波域。使用提取的信息作为特征和预定义的距离函数，我们能够识别和量化减少或失真版本的数据中的质量损失，从而无需访问原始数据。我们的特征表示自然地以多尺度的形式组织，这有助于对具有不同分辨率的数据进行质量评估。该特征可以有效地压缩尺寸。我们已经在各种大小和特征的科学和医学数据集上试验了我们的算法。我们的研究结果表明，该功能的大小不增加的原始数据的大小成比例。这确保了我们算法的可扩展性，并使其非常适用于大规模数据集的质量评估。此外，该功能可以用于修复减少或失真的数据，以提高质量。最后，我们的方法可以被视为一种新的方法来评估不同版本的数据所引入的不确定性。
— Quality assessment plays a crucial role in data analysis. In this paper, we present a reduced-reference approach to volume data quality assessment. Our algorithm extracts important statistical information from the original data in the wavelet domain. Using the extracted information as feature and predefined distance functions, we are able to identify and quantify the quality loss in the reduced or distorted version of data, eliminating the need to access the original data. Our feature representation is naturally organized in the form of multiple scales, which facilitates quality evaluation of data with different resolutions. The feature can be effectively compressed in size. We have experimented with our algorithm on scientific and medical data sets of various sizes and characteristics. Our results show that the size of the feature does not increase in proportion to the size of original data. This ensures the scalability of our algorithm and makes it very applicable for quality assessment of large-scale data sets. Additionally, the feature could be used to repair the reduced or distorted data for quality improvement. Finally, our approach can be treated as a new way to evaluate the uncertainty introduced by different versions of data.