Two algorithms are presented for the detection of gradual transitions in video sequences. The first is a dissolve detection algorithm utilizing certain properties of a dissolve's trajectory in image-space. It is implemented both as a simple threshold-based detector, and as a parametric detector by modeling the error properties of the extracted statistics. The second is an algorithm to detect a wide variety of wipes based on image histogram characteristics during such transitions. Both algorithms operate in the compressed domain, requiring only partial decoding of the compressed video stream. Experiments show the algorithms perform as well as-and in some cases, better than-full-frame methods, on a wide variety of gradual transitions, and can operate significantly faster than real-time.

The well-known Principle of Least Privilege states that a program should run with the minimal authority that it requires to get the job done, and no more. However, application of the principle has been left to software developers, developers of installation procedures, and system administrators with few tools to assist them. How much privilege does a given program need? How do you know if you write a program that uses too much privilege or install a program with too little? Empirical privilege profiling provides a partial answer to this question by tracking a program's actual use of resources, which can be used as a guide during program development and installation, as well as for detecting intrusions and providing assurance for mobile code. In this paper, we introduce the concept of dealing with privilege as a measurable quantity, rather than in terms of a "rule of thumb."

The reliable and efficient transmission of high-quality variable bit rate (VBR) video through the Internet generally requires network resources be allocated in a dynamic fashion. This includes the determination of when to renegotiate for network resources, as well as how much to request at a given time. The accuracy of any resource request method depends critically on its prediction of future traffic patterns. Such a prediction can be performed using the content and traffic information of short video segments. This paper presents a systematic approach to select the best features for prediction, indicating that while content is important in predicting the bandwidth of a video hit stream, the use of both content and available short-term bandwidth statistics can yield significant improvements. A new framework for traffic prediction is proposed in this paper; experimental results show a smaller mean-square resource prediction error and higher overall link utilization.

Two algorithms are presented for the detection of gradual transitions in video sequences. The first is a dissolve detection algorithm utilizing certain properties of a dissolve's trajectory in image-space. The second is an algorithm to detect a wide variety of wipes based on the image histogram characteristics during such transitions. Both algorithms operate in the compressed domain, requiring only partial decoding of the compressed video stream. Experiments show the algorithms perform well in detecting a wide variety of gradual transitions, and at a significant reduction in computation time when compared with full-frame methods.

Dynamic resource allocation is critical in the transmission of VBR video. Our study shows that content is one of the major factors that controls the bandwidth of the video bit-stream, yet content alone may not be sufficient in predicting future traffic and in determining how much resource to request. A new framework of traffic prediction is proposed, taking into account both content features and available short-term bandwidth statistics.