译文摘录：

基于集群机的并行绘制是提高图形系统性能和分辨率的有效方法，图像深度合成是sort-last并行绘制系统的关键步骤。本文提出了一套sort-last并行绘制系统中的图像深度融合策略，该策略针对图像深度数据的特点，提出了一个基于模板的深度信息LZW无损压缩方法和图像深度信息插值方法；针对图像颜色数据的特点，给出了一个基于Jpeg的图像颜色信息有损压缩方法；针对图像深度合成步骤，给出了一个基于透明度的融合方法。实验结果表明，该策略应用于sort-last并行绘制系统中，优于其它基于压缩的图像合成策略。

The cluster computer based parallel rendering is an effective method to improve graphics system performance and resolution, the image depth fusion is the key step of sort-last parallel rendering system. This paper puts forward a set of strategies of image depth fusion in sort-last parallel rendering system. Regarding this feature of image depth data, this strategy puts forward template based depth information LZW non-destructive compression method and image depth information interpolation method; regarding the feature of image color data, it puts forward Jpeg based image color information destructive compression method; regarding the image depth synthesis step, it gives a transparence based fusion method. The experimental result shows that this strategy is better than other compression based image synthesis strategies if it is used in sort-last parallel rendering system.

在虚拟现实技术的研究领域，高性能和高分辨率场景绘制的传统方法是使用大型的专业图形处理机，如SGI图形机等，而随着并行绘制概念的引入，绘制系统有了长足的发展。基于PC集群的并行绘制系统的研究已经成为构建高性能、高精度要求的图形系统的关键方法。1994年Molnar等人根据并行任务划分的阶段与典型图形绘制流程的关系，将并行绘制系统划分为三种类型：sort-first，sort-middle，sort-last[1]。sort-first系统按照屏幕空间进行任务划分，但是图元在屏幕上的分布不均匀及图元复杂程序的差异容易导致系统负载不平衡；sort-middle一般适用于硬件实现；而sort-last并行绘制系统，不对图像空间进行划分，各绘制处理器互相独立操作，直到光栅化阶段的最后才会重新分布像素。

In the research field of virtual reality technology, the traditional method of high performance and high resolution scene rendering is to use large scale professional graphics processors, e.g., SGI graphics processor, while with the introduction of parallel rendering concept, the rendering system has made rapid development. The research on PC cluster based parallel rendering system has become the key method constructing graphics system of high performance and high precision requirements. In 1994, Molnar and others divided the parallel rendering system into three types according to the relationship between the stages of parallel task division and typical graphics rendering flow: Sort-first, sort-middle and sort-last. Tasks of sort-first system are divided according to screen space, however, the uneven distribution of primitives on screen and difference of complex procedures of primitives easily leads to the imbalance of system load; sort-middle is usually applicable to hardware realization; while sort-last parallel rendering system does not divide graphics space, each rendering processor is independent mutually and redistribute pixels till the end of rasterization stage.

sort-last并行绘制系统因场景数据的不均匀分布引起的负载不平衡较小，但是却增加了图像融合的处理步骤，图像的传输和融合是sort-last并行绘制系统的主要开销和瓶颈，因此如何缩短图像数据的传输时间，优化图像的融合结果成为sort-last并行绘制系统的重要问题。

Sort-last parallel rendering system causes little unbalanced load due to the uneven distribution of scene data, while increases the processing steps of image fusion. The transmission and fusion of image is the main overhead and bottleneck of sort-last parallel rendering system, so how to shorten the transmission time of image data and optimize the fusion result of image has become an important issue of sort-last parallel rendering system.

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