A Content-based Selective Enhancement Layer Dropping Algorithm For FGS Streaming Using Nearest Feature Line Method

摘要

This paper proposes a new content-based rate shaping algorithm for Fine Granular Scalable scheme (FGS). FGS has been adopted by the MPEG-4 video standard as the core compression tool for video streaming over Internet, and has enabled a wide range of multimedia applications. However, most of current video streaming technologies only protect the video stream by rate allocation or CRC means, based on the bandwidth of the network. As a result, the video quality that user expected is unrelated with the content of video. Our approach is innovative in that it is based on video content analysis and extraction of the information of video content. Firstly, we evaluate the importance of the video sequence by using NFL (Nearest Feature Line) method. Then we drop the enhancement layer in term of the importance, which is decided by the bits sent out that meet the current bandwidth of network. The experimental results indicate that our layer dropping method not only improves the performance of FGS to 0.2 dB, but also enhances the subjective quality of video effectively.