面向Web科学可视化的临近空间数据视频化方法

临近空间在科学、经济和军事等领域具有重要价值，高效便捷地进行临近空间数据Web科学可视化和信息提取是临近空间数据应用不断深入和扩展的必然需求，但是，已有的临近空间数据类型繁杂和数据量巨大，成为Web传输和实时可视化的关键瓶颈。从数据组织方法角度展开了研究，根据临近空间数据的特点，结合视频压缩原理，提出了将临近空间数据进行分解、插值和色彩空间变换处理形成图像后，选取适宜视频压缩编码方法将图像编码为视频。实验结果表明:所提方法可以实现临近空间数据在Web环境下的高效组织，在保障可视化质量的前提下通过降低数据网络传输量实现了临近空间数据的实时可视化需求。研究结果可以直接解决临近空间数据Web实时可视化问题，将为临近空间科学研究和知识发现提供可视化理论和技术支持，并为类似海量数据Web科学可视化提供方法借鉴。 

Video formatting method of near-space data for Web scientific visualization

As a transitional region from sky to space, near-space has important value in the fields of science, economy and military. With the development of remote sensing technology and the continuous development of mode simulation, the massive near-space data of higher spatial and temporal resolution is increasing. On the other hand, efficient and convenient Web scientific visualization and information extraction for near-space data are inevitable requirements for the continuous deepening and expansion of near-space data applications. However, the existing data types are cumbersome and the amount of data is huge in near-space, which becomes the key bottleneck of Web transmission and real-time visualization. This paper focuses on the visualization of near-space data on the Web digital globe, and studies it from the point of data organization methods. According to the characteristics of near-space data, combined with the principle of video compression, we propose to decompose, interpolate and color space transform the near-space data to form an image, and then select the appropriate video compression coding method to encode the image into video. Experiment results show that this method can be implemented efficiently. The video organization of spatial data in the Web environment realizes the real-time visualization of near-space data by reducing the amount of data network transmission while ensuring the quality of visualization. The research results in this paper can directly solve the real-time visualization problem of near-space data on Web, provide visual theory and technical support for the near-space science research and knowledge discovery, and provide a reference for the Web scientific visualization on similar massive data.