As people's life quality have been improved significantly, the traditional 2D video technology can not meet people's
urgent desire for a better video quality, which leads to the rapid development of 3D video technology. Simultaneously
people want to watch 3D video in portable devices,. For achieving the above purpose, we set up a remote stereoscopic
video play platform. The platform consists of a server and clients. The server is used for transmission of different formats
of video and the client is responsible for receiving remote video for the next decoding and pixel restructuring. We utilize
and improve Live555 as video transmission server. Live555 is a cross-platform open source project which provides
solutions for streaming media such as RTSP protocol and supports transmission of multiple video formats. At the
receiving end, we use our laboratory own player. The player for Android, which is with all the basic functions as the
ordinary players do and able to play normal 2D video, is the basic structure for redevelopment. Also RTSP is
implemented into this structure for telecommunication. In order to achieve stereoscopic display, we need to make pixel
rearrangement in this player's decoding part. The decoding part is the local code which JNI interface calls so that we can
extract video frames more effectively. The video formats that we process are left and right, up and down and nine grids.
In the design and development, a large number of key technologies from Android application development have been
employed, including a variety of wireless transmission, pixel restructuring and JNI call. By employing these key
technologies, the design plan has been finally completed. After some updates and optimizations, the video player can
play remote 3D video well anytime and anywhere and meet people's requirement.
Stereo vision is a hot research topic in the field of computer vision and 3D video display.Disparity map is one of the
most crucial steps. A novel constant computational complexity algorithm based on separable successive weight
summation (SWS) is presented. The proposed algorithm eliminates iteration and support area independently, which saves
computation and memory space .The similar measure of gradient is also applied to improve the original algorithm. Image
segmentation and edge detection is used for the stereo matching to accelerate the speed and improve the accuracy of
matching algorithm.The image of edge is extracted to reduce the search scope for the stereo matching algorithm. Dense
disparity map was obtained through local optimization.Experimental results show that the algorithm is efficient and can
well reduce the matching noise and improve the matching precision in depth discontinuities and low-texture region.