目的 针对异构网络环境下，不同终端用户对遥感影像质量的不同需求而导致的影像数据量过大、传输及显示延迟过长等问题，提出一种在线压缩—实时传输—实时解压缩的遥感影像渐进传输模型。方法 模型采用多线程流水线同步处理的加速算法，将基于质量渐进压缩的SPIHT算法与多线程流水线技术相结合，在VC++环境下，将遥感影像在线压缩成码流，在压缩的同时，启动多线程采用Socket信道对压缩码流实时发送，客户端收到码流后，利用多线程实时解压缩并显示。通过采用多线程技术，使得压缩、传输和解压缩同步进行，从而减少了整体处理时间。结果 实验结果表明，提出的实时压缩渐进传输模型，在不影响影像质量的前提下，算法处理速度提高近2倍。每个渐进分层影像与原影像的相似度比多分辨率渐进压缩分层影像与原影像的相似度平均增加20%。结论 该模型有效地解决了遥感影像渐进传输过程中压缩、传输和解压缩的不同步问题，从而提高了渐进传输效率。与多分辨率渐进传输比较，此渐进传输模型具有更好的视觉效果。

Objective To meet the different needs of different users to the quality of remote sensing images leading to a series of problems, such as a large amount of image data and transmission and display delay in heterogeneous network environments, an online remote sensing image progressive transmission model is constructed in which remote sensing image compression and decompression are synchronized with transmission. Method At the same time, a pipeline-based multi-thread acceleration scheme with SPIHT algorithm, which is a quality progressive method, is proposed through solving the asynchronous problem between compression, decompression, and transmission to improve the efficiency of remote sensing progressive transmission. In the VC++ platform, the proposed model was implemented based on a socket communication channel and SPIHT, which can provide a qualitative progressive code stream. First, a given image was compressed into a code stream by SPIHT. Then, the socket channel was used for sending the code stream in real time. For the client, it decompressed the code stream and displayed the reconstructed image as soon as it received the code stream. The real-time system was realized by multi-threading technology, which guaranteed that the compression, transmission, and decompression were processed synchronously. Therefore, the whole processing time can be reduced. Result Experimental results show that the whole processing speed has been improved nearly twice without reducing image quality by using the proposed progressive transmission of the real-time compression model. Compare with multi-resolution progressive method, the similarity index has improved an increase of 20% between each progressive layer of image and the original image in our quality progressive method. Conclusion The asynchronous problem during the processing of compression-transmission-decompression for remote sensing images was solved in the new model, and the transmitting efficiency has greatly improved. On the other hand, this proposed progressive transmission model has done better in visual effects as contrasted with the multi-resolution progressive transmission.