一种适用于网上视频监控的MPEG-4码流技术

针对IP网络(如因特网)带宽随时变化的特性,传输视频时,为了有效地利用现有带宽,尽可能提高视觉质量,该文在MPEG-4精细可伸缩性视频编码(FGS)的选择性增强技术基础上提出一种基于运动矢量的自适应选择性增强算法。通过提取、分析基本层运动矢量,自适应地确定目标区域,并将结果在增强层编码用于选择性增强。使FGS编码实现基于内容的增强和优先传输感兴趣区域(目标区域)。实验表明该算法使目标区域在不同的码率下都能获得相对最佳的视频质量,适合网上视频监控之类背景静止而人们仅对场景中运动的人或物感兴趣的应用。     

Key Frame Extraction Using Unsupervised Clustering Based on a Statistical Model

This paper proposes a novel algorithm for extracting key frames to represent video shots. Regarding whether, or how well, a key frame represents a shot, different interpretations have been suggested.We develop our algorithm on the assumption that more important content may demand more attention and may last relatively more frames. Unsupervised clustering is used to divide the frames into clusters within a shot, and then a key frame is selected from each candidate cluster. To make the algorithm independent of video sequences, we employ a statistical model to calculate the clustering threshold. The proposed algorithm can capture the important yet salient content as the key frame. Its robustness and adaptability are validated by experiments with various kinds of video sequences.