目的 多分辨率体绘制是解决海量数据体绘制的一种有效方法。但对于数据散乱分布、同质区域较小的体绘制数据（比如物探领域的地震信号数据），传统的基于香农熵或均方差的多分辨率方式均难以有效实现降低数据量的效果，为此提出了一种基于目标特征的多分辨率体绘制方法。方法 以数据体中的目标特征为指导，适当降低非目标区域的分辨率，在尽可能不丢失其目标区域信息的情况下，实现有效的多分辨率体绘制。结果 本文方法能够在目标保证数据量的前提下，尽可能地通过丢弃非目标区域的信息量，进而保护数据体的关键信息，以得到较好的绘制效果。结论 实验结果表明，本文方法与传统方法相比能够更好地保证关键区域绘制效果，同时进一步地降低用于绘制的数据量。

Objective Multi-resolution volume rendering is an effective method to solve the problem of massive data volume rendering. We assume that there are some homogeneous regions with smaller variance or Shannon entropy in the data. By reducing the resolution of these areas, the overall amount of data can be reduced. However, for some kind of data, such as seismic data, there are only few homogeneous regions. For such data, traditional methods can hardly achieve the goal of multi-resolution. Method In this paper, a target-based multi-resolution volume rendering method is proposed based on the traditional method. Its basic idea is to use the target feature of the data as a guide to find the target areas. In some cases, the Shannon entropy and variance can only reflect the numerical changes, but not the information, which is required by the users. Therefore, this method uses the target feature to find the target areas, which are of interest for the user. By appropriately reducing the resolution of non-target areas after processing the data in the traditional way the compression ratio is improved. Result This method can meet the demand of computer memory by reducing the amount of data for non-critical regions and to protect the data in target regions as far as possible. In this way, we can achieve multi-resolution volume rendering effectively without losing the critical information of target areas. Under the premise of guarantee the amount of data, this method can get a better rendering results by dropping the amount of information in the non-target area, and to ensure high drawing quality of the target region. Conclusion Experimental results show that compared with traditional methods, the proposed method can get better drawing effects in critical areas, while further reducing the amount of data used to draw.