KEYWORDS: Computer security, Databases, Information security, Systems modeling, Cryptography, Data analysis, Lithium, Legal, Video, Data communications
Most existing license management schemes for DRM systems do not support the protection of user privacy; moreover, some other schemes such as PrecePt can only bind the license to a specified device though privacy protection is concerned. In this paper, we propose a flexible license management protocol named FLMP (Flexible License Management Protocol) which provides a more powerful and flexible license management scheme to satisfy the various requirements of users for privacy protection and use convenience.
KEYWORDS: Video, Multimedia, Analytical research, Semantic video, Video coding, Internet, Personal digital assistants, Visualization, Motion estimation, Mobile devices
Along with growing up of wireless network and mobile devices, more and more users expect to access multimedia content not only by PC terminal, but also through those pervasive devices. In this paper, we proposed a content-based video-streaming framework for universal multimedia access (UMA). The framework that mainly combines the video analysis with the video streaming technology can make the pervasive device access the multimedia information at anywhere, anytime and anyhow. Our contribution of this paper is to streaming video data according to the video analysis result, the video data during transmission is no more “meaningless information” bits. We have implemented a UMA server, which customizes the video stream according to the video analysis result and the capabilities of client, and a UMA client that receives the adapted video stream.
KEYWORDS: Video, Video compression, Computer programming, Video coding, Scalable video coding, Internet, Visualization, Standards development, Information visualization, Error control coding
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.
A projected growth of digital video databases and hence the importance of management strategies creates a challenging task for researchers. Index and search technology must enable access to humanly meaningful segments within video data streams if this increase of content is to be at all useful. The fundamental task in video analysis and retrieval then is to facilitate the human computer interface by bridging the worlds of low-level physical features in media and high-level human description. In this paper, we have discussed the key issues about design and implement a video management system based on MPEG-7. From this, we introduce Tsinghua Video Find-It System (TVFind), a prototype platform for retrieving and browsing within a video database system over web.
This paper proposes the extraction scheme of global motion and object trajectory in a video shot for content-based video retrieval. Motion is the key feature representing temporal information of videos. And it is more objective and consistent compared to other features such as color, texture, etc. Efficient motion feature extraction is an important step for content-based video retrieval. Some approaches have been taken to extract camera motion and motion activity in video sequences. When dealing with the problem of object tracking, algorithms are always proposed on the basis of known object region in the frames. In this paper, a whole picture of the motion information in the video shot has been achieved through analyzing motion of background and foreground respectively and automatically. 6-parameter affine model is utilized as the motion model of background motion, and a fast and robust global motion estimation algorithm is developed to estimate the parameters of the motion model. The object region is obtained by means of global motion compensation between two consecutive frames. Then the center of object region is calculated and tracked to get the object motion trajectory in the video sequence. Global motion and object trajectory are described with MPEG-7 parametric motion and motion trajectory descriptors and valid similar measures are defined for the two descriptors. Experimental results indicate that our proposed scheme is reliable and efficient.
For more efficiently organizing, browsing, and retrieving digital video, it is important to extract video structure information at both scene and shot levels. This paper present an effective approach to video scene segmentation based on probabilistic model merging. In our proposed method, we regard the shots in video sequence as hidden state variable and use probabilistic clustering to get the best clustering performance. The experimental results show that our method produces reasonable clustering results based on the visual content. A project named HomeVideo is introduced to show the application of the proposed method for personal video materials management.
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