10358457
A new adaptive interframe transform coding using directional classification
Interframe transform coding is affected not only by the statistics of spatial details within a frame, but also by the variation of the amount of movement and other temporal activities in different regions of the image sequence. Therefore, adaptive techniques have to be used in order to achieve good image quality. In this paper, we propose a new version of the adaptive interframe coding method, namely directional classification, which is based on image sequence statistics. Blocks with different perceptual features such as edges and high motion activity are categorised to different classes. Then, a new adaptive quantization, associated with appropriate scanning and Huffman coding, are employed based on the classification map. Coding tests using computer simulation show this technique is indeed very efficient.<<ETX>>
17360670
Priority search technique for MPEG-4 motion estimation of arbitrarily shaped video object
One of the main differences between MPEG-4 and previously standardized video coding schemes is the support of arbitrarily shaped video objects, for which most of the existing fast motion estimation algorithms are not suitable. The conventional fast motion estimation algorithm works well for opaque macroblocks, but not in the case of a boundary macroblock which contains a large number of local minima on its error surface. We propose a fast search algorithm which incorporates the binary alpha-plane to predict accurately the motion vectors of boundary macroblocks. Besides, these accurate motion vectors can be used to develop a novel priority search algorithm which is an efficient search strategy for the remaining opaque macroblocks. Experimental results show that, compared to the conventional methods, our approach requires a low computational complexity and provides a significant improvement in terms of accuracy in motion-compensated video object planes.
6180081
New adaptive partial distortion search using clustered pixel matching error characteristic
The partial distortion search is a particularly attractive fast block-matching algorithm, because it introduces no prediction error as compared with the full-search algorithm. It reduces the number of necessary matching evaluations for every searching point to save computation. In the literature, many researches have tried to improve block-matching algorithms by making use of an assumption that pixels with larger gradient magnitudes have larger matching errors on average. Based on a simple analysis, we have found that on average, pixel matching errors with similar magnitudes tend to appear in clusters for natural video sequences. By using this clustering characteristic, we propose an adaptive partial distortion search algorithm which significantly improves the computational efficiency of the original PDS. This approach is much better than other pixel gradient based adaptive PDS algorithms. In addition, our proposed algorithm is suitable for motion estimation of both opaque and boundary macroblocks of an arbitrary shaped object in MPEG-4 coding.
18724966
An Aided Congestion Avoidance Mechanism for TCP Vegas
TCP Vegas detects network congestion in the early stage and successfully prevents periodic packet loss that usually occurs in TCP Reno. It has been demonstrated that TCP Vegas achieves much higher performance than TCP Reno in many aspects. However, TCP Vegas cannot prevent unnecessary throughput degradation when congestion occurs in the backward path, it passes through multiple congested links, or it reroutes through a path with longer round-trip time (RTT). In this paper, we propose an aided congestion avoidance mechanism for TCP Vegas, called Aid-Vegas, which uses the relative one-way delay of each packet along the forward path to distinguish whether congestion occurs or not. Through the results of simulation, we demonstrate that Aid-Vegas can solve the problems of rerouting and backward congestion, enhance the fairness among the competitive connections, and improve the throughput when multiple congested links are encountered.
31127718
Detecting a Security Disturbance in Multi Commodity Stochastic Networks
Here is a multi-criteria approach to identify and evaluate network-flow monitoring strategies in a stochastic environment. We propose an analytical approach for assessing flow disturbance, based on limited sampling of arc-flow information in multi commodity, or multiple origin-destination (O–D), networks. A disturbance is defined here as abnormal network traffic beyond routine fluctuations. The network is characterized by arcs that are available with certain probabilities, suggesting a busy packet-switching/circuit-switching network, or simply network with failing components. Given the huge state space of such stochastic networks, our first objective is to bound the expected flow for computational efficiency. This is accomplished by extending available single-commodity-network results to the multi commodity case. The second objective is to determine the best placement of flow monitors to obtain the most accurate estimates of traffic flow, as represented by O–D pair traffic volumes in order to detect a disturbance. We followed a multi-criteria approach in defining and evaluating all possible monitor-placement strategies satisfying monitor availability. To assess a traffic pattern, O–D volumes are estimated using the lp-metric for p = 1, 2 and ∞, representing a decision-maker's risk-preference structure. The final objective is to define a disturbance metric providing confident assessments on the occurrence of a flow disturbance. In this regard, we use linear-regression to generate confidence intervals around the expected flow for each O–D pair. This is supplemented by the Fisher Method of Randomization, a non-parametric test, for small networks. Unlike the Fisher-test results, it appears the linear-regression “disturbance metric” is very sensitive to detecting “disturbance.” Preliminary results suggest that among the three lp metrics in O–D estimation, it appears that the best is the l2-metric, which provides the appropriate sensitivity and accuracy. Counter to intuition, it is also observed that the “disturbance” estimation error generally decreases as the number of sampled arcs decreases.
28992997
Creating reusable WebQuest objects with WebQuest authoring engine
WebQuest is a constructivist lesson format for Web-enabled inquiry-based learning. A WebQuest exists in form of a Web site that contains a collection of Web pages. In this paper, we define the "WebQuest Object" which is basically a learning object. We also present the WebQuest authoring engine which enables one to generate WebQuest objects without the knowledge in Web page construction and publishing. The WebQuest authoring engine also allows ones to edit the metadata of the WebQuest so as to facilitate the reuse of the WebQuest objects.
894881
Performance improvement of TCP Vegas over heterogeneous networks
Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.
10812167
A TCP-Friendly Stateless AQM Scheme for Fair Bandwidth Allocation
Queue management, bandwidth share, and congestion control are very important to both robustness and fairness of the Internet. In this article, we investigate the problem of providing a fair bandwidth allocation to those flows that share congested link in a router. A new TCP-friendly router-based AQM (active queue management) scheme, termed WARD, is proposed to approximate the fair queueing policy. WARD is a simple packet dropping algorithm with a random mechanism, and discriminates against the flows which transmit more packets than allowed. By doing this, it not only protects TCP connections from UDP flows, but also solves the problem of competing bandwidth among different TCP versions such as TCP Vegas and Reno. In addition, WARD works quite well for TCP flow isolation even with different round trip times. In other words, WARD improves the unfair bandwidth allocation properties. Furthermore, it is stateless and easy to implement, so WARD controls unresponsive or misbehaving flows with a minimum overhead