本文選題：多屬性體數(shù)據(jù) + 四階張量近似　； 參考：《電子科技大學》2017年碩士論文

【摘要】：隨著數(shù)據(jù)規(guī)模的越來越大,壓縮體繪制是解決大規(guī)模數(shù)據(jù)體繪制的有效方法。在眾多壓縮體繪制方法中,基于張量近似的壓縮體繪制方法在壓縮率和繪制效果方面具有良好的性能。多屬性融合可視化是降低目標多解性、凸顯目標特征的有效手段。本文針對多屬性壓縮體繪制問題開展研究,并取得了一定的成果。該研究具有較大的理論價值和實際應用價值。針對多屬性融合壓縮體繪制問題,在現(xiàn)有三階張量近似的壓縮體繪制和多屬性融合體繪制的基礎上,探索四階張量近似的壓縮體繪制方法。同時,針對張量近似中的秩截斷對壓縮率和壓縮效果有較大影響問題開展研究。主要的工作和創(chuàng)新如下:1.提出了基于四階張量近似的多屬性壓縮融合體繪制方法。針對多屬性壓縮融合體繪制問題,目前還沒有有效的方法和手段,但可以利用三階張量近似分別對多個屬性體數(shù)據(jù)進行壓縮,并在體繪制階段進行融合繪制。這種方法壓縮效率較低,無法充分利用多屬性數(shù)據(jù)之間的相關性。基于此,本文提出了基于四階張量近似的多屬性融合體繪制方法。其基本思想是將多屬性體看作一個四維數(shù)據(jù)并進行基于四階張量近似的壓縮、融合。仿真結果表明,本方法在損失極少的數(shù)據(jù)信息的情況下,數(shù)據(jù)的壓縮率有明顯地提升;2.提出了基于各向異性秩截斷的高階張量近似方法。在高階張量近似中,通常是各個方向上的采用相同的秩。而實際數(shù)據(jù)往往在不同方向上的相關性具有各向異性的特點。因此本文提出了基于各向異性秩截斷的高階張量近似方法。該方法通過對各個方向上奇異值分布的分析,計算各個方向的秩截斷大小,得到不同方向上的截斷秩,以提高壓縮效率和壓縮效果。仿真結果表明,本文提出的基于各向異性秩截斷的高階張量近似方法在滿足壓縮率的情況下,壓縮效果有所提升。綜上所述,本文在仿真平臺上實現(xiàn)上述方法。仿真結果表明,本文提出的方法在多屬性體數(shù)據(jù)壓縮融合體繪制中有效提高了數(shù)據(jù)的壓縮率。在針對各向異性數(shù)據(jù)張量近似截斷秩選取的問題中,本文提出的方法在不同壓縮率限定條件下有效提高數(shù)據(jù)的壓縮效果,在多屬性數(shù)據(jù)的可視化中有著很好的應用前景。
[Abstract]:With the increasing scale of data, compressed volume rendering is an effective method to solve large scale data volume rendering. Among many compressed volume rendering methods, the compressed volume rendering method based on Zhang Liang approximation has good performance in compression ratio and rendering effect. Multi-attribute fusion visualization is an effective method to reduce the multi-solvability of targets and highlight the features of targets. In this paper, the problem of multi-attribute compression volume rendering is studied, and some achievements have been made. This research has great theoretical value and practical application value. Aiming at the problem of multi-attribute fusion compression volume rendering, based on the existing third order Zhang Liang approximation compression volume rendering and multi-attribute fusion volume rendering, the fourth order Zhang Liang approximation compression volume rendering method is explored. At the same time, the influence of rank truncation on compression ratio and compression effect in Zhang Liang approximation is studied. The main tasks and innovations are as follows: 1. A method of multi-attribute compression fusion volume rendering based on fourth order Zhang Liang approximation is proposed. There are no effective methods and methods for multi-attribute compression fusion volume rendering, but it is possible to compress multiple attribute volume data using third-order Zhang Liang approximation and perform fusion rendering in volume rendering phase. The compression efficiency of this method is low and the correlation between multi-attribute data can not be fully utilized. Based on this, a multi-attribute fusion volume rendering method based on four-order Zhang Liang approximation is proposed. The basic idea is to treat the multi-attribute body as a four-dimensional data and to compress and fuse it based on the fourth-order Zhang Liang approximation. The simulation results show that the compression ratio of the data can be significantly improved when the data information is lost very little. A high order Zhang Liang approximation method based on anisotropic rank truncation is proposed. In higher order Zhang Liang approximation, the same rank is usually used in each direction. The correlation of actual data in different directions is anisotropic. Therefore, a high order Zhang Liang approximation method based on anisotropic rank truncation is proposed. By analyzing the distribution of singular values in different directions, the rank truncation of each direction is calculated, and the truncation rank in different directions is obtained to improve the compression efficiency and compression effect. The simulation results show that the proposed high order Zhang Liang approximation method based on anisotropic rank truncation can improve the compression performance when the compression ratio is satisfied. To sum up, this paper implements the above method on the simulation platform. Simulation results show that the proposed method can effectively improve the compression rate of multi-attribute volume data compression fusion volume rendering. In order to solve the problem of selecting approximate truncation rank of Zhang Liang for anisotropic data, the method proposed in this paper can effectively improve the compression effect of data under different compression ratios, and has a good application prospect in the visualization of multi-attribute data.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;O183.2

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