Scalable Video Coding for Vision Based Subway Platform Monitoring System

The paper proposes a scalable video coding scheme for vision based subway platform monitoring system. The proposed scheme is designed for providing flexible video service among various display environments, such as displays of total traffic control, station employees and train driver and so on. The proposed scheme uses enhanced compatible coding scheme for predicting P- and B-types of pictures in the right-view sequence. It predicts matching block by interpolating both motion and disparity predicted macro-blocks. To provide flexible stereo video service, we define both temporally and spatially scalable layers for each eyes-view by using the concept of spatio-temporal scalability. The experimental results show the efficiency of proposed coding scheme by comparison with already known methods and the advantages of disparity estimation in terms of scalability overhead. According to the experimental results, we expect the proposed functionalities will play a key role in establishing highly flexible stereo video service for ubiquitous computing environment where devices and network connections are heterogeneous