基于对象的精细可伸缩性编码

医学影像是医生用于诊断的重要工具，高质量的影像对于远程医学在Internet上的普及具有重要意义。然而，当影像在丢包网络如Internet上传输时，接收端有时需要经过长时间的等待才能获得整个码流来重建影像。对于需要实时获得影像的医生，这是无法忍受的。本研究提出一种基于对象的精细可伸缩性编码方法，该方法将对象编码与位平面编码相结合。一方面，这种方法具有精细增强图像质量的能力；另一方面，它又可以通过指定对象优先级和提升位平面来更为灵活地控制码流结构。首先描述整个编码框架，然后对该框架所涉及的位平面编码、选择性增强和码流结构等关键技术分别进行重点讨论，最后给出不同码流结构在不同断点处的重建图像之间的比较。

The Fine-Granular-Scalable Coding Based on the Image Object

Medical images are one vital diagnosis tool for physicians,high image quality is important to the prevalence of telemedicine over the Internet.However,when medical images transmit over the packets loss channel such as Internet,the receiver may wait for a long time to get the total bitstream to reconstruct the images.It is intolerable to the physicians who need to get the images at realtime.In this paper,we presented a new Fine-Granular-Scalable coding based on the image objects,which incorporated image object coding with bitplane coding.This method not only has the ability of enhancing the image quality granularly,but also has more flexibility in controlling the bitstream structure than the other coding method by setting the priority of the image object and upshifting the bitplane.We described the coding framework and separately discussed the key techniques such as bitplane coding,selective enhancement and bitstream structure.The comparison between the different images reconstructed according to the different truncated bitstream was discussed as well.