Research On Video Transcoding For Mobile Video Surveillance

Video surveillance plays an important role in the industry. The emergence of mobile video surveillance further expands the scope of its application. People can use mobile phones and PDAs to watch the surveillance scenarios at anytime in anywhere. However, compared to the traditional video surveillance, mobile video surveillance suffers the problems of poor picture quality and long latency, due to the narrow wireless network bandwidth and limited mobile device computational power. As the key technology of mobile video surveillance, video transcoding faces many challenges. There is important theoretical and practical value to research on the key technologies of video transcoding.When the incoming coded video stream with specific format is not fit for the current network or user requirement, video transcoding can transform the coded stream from one format to the required one. In this paper, we make some researches on the key technologies of transcoding, mainly containing syntax transcoding, bit-rate transcoding, spatial resolution transcoding, error resilient transcoding and multi-objective transcoding. The research is based on the latest video coding standard, H.264, in consideration of the narrow bandwidth of wireless network. Moreover, the practical applications usually demand on a number of transcoding objectives, hence we also try to achieve optimal performance in a joint multi-objective environment. To summarize, the research includes the following aspects:Firstly we briefly review some basic concepts and the development history of video compressing, especially the latest video coding standard H.264. Then we introduce video transcoding, including its technical objectives, framework structures and classification. Other closely related technologies are also introduced.Regarding the syntax transcoding, the MPEG-4 to H.264 syntax transcoding is studied. By analyzing the similarities and differences between H.264 and MPEG-4, the research objectives of MPEG-4 to H.264 transcoding are introduced. We mainly focus on mode decision and motion estimation modules, which have the highest computational complexity. Three candidate mode optimization technologies are proposed. They can reduce the number of candidate modes or directly make the mode decision. We also propose a fast multi-reference-frame based motion estimation algorithm, which exploits the temporal correlation as well as the spatial correlation. Compared to the reference methods, which only take the temporal correlation, the proposed algorithm has much lower computational complexity. Moreover, an adaptive search range selection method is also proposed to further improve the transcoding speed.Regarding the bit-rate transcoding, we adopt rate control as its realization method. We firstly analyze the reasons that why bit-rate transcoding and rate control can be integrated. Then we make an effort to research on the low complexity rate control algorithm. Based on the features of video surveillance, a frame level rate control algorithm is proposed. It can provide smoother picture quality. Moreover, without rate-distortion model, a look-up table based macroblock level rate control algorithm is also proposed. The proposed macroblock level algorithm can effectively reduce the computational complexity as well as ensuring the picture quality.Regarding the spatial resolution transcoding, we analyze the characteristics of the H.264, and mainly focus on the coding type decision, motion vector reconstruction and mode decision modules. An arbitrary motion vector reconstruction method for H.264 spatial resolution down-sampling is proposed, with which, the predicted motion vector can be achieved. To further speed up the transcoding, a bottom-up merging mode decision algorithm is also proposed. It utilizes the proposed early-stop and motion vector reconstruction methods, and achieves very substantial increase in transcoding speed at a cost of a little picture quality lost.Regarding the error resilient transcoding, the ROI region protection scheme is studied. A macroblock sensitivity degree model is proposed. With the coding type, motion vector and other information, the model gives the impact value of video quality lost for a macroblock, then sorts the impact values and picks out the ROI macroblocks with low computational complexity. Then two ROI region protection strategies, intra-refresh and motion vector protection methods, are analyzed and their performance is also compared.Finally, this paper studies the multi-objective transcoding, including the composition of bit-rate, spatial resolution, temporal resolution, error resilient and syntax transcoding. Simply cascade multiple transcoders will not reach the optimal performance. We analyze the implementation sequence of the motion vector reconstruction in the joint-transcoder with spatial, temporal resolution and syntax transcoding. Based on bit-rate transcoding, three transcoder structures with different functions and computational complexies are proposed to apply to different practical applications with diversity setting.