An Adaptive-Filtering Policy for Distributed Virtual Environments

In this paper, an adaptive-filtering policy for distributed virtual environments (DVEs) was brought forward. It took advantages of two traditional filtering policies (static-filtering policy and equidistance-transmission policy) and could adaptively select the fittest filtering policy corresponding to current motion characteristic of the virtual object in DVEs. An experiment was designed to compare this filtering policy with the two traditional filtering policies on task performance. Objective (task completion time, state updates transmitted and state updates per second) and subjective (filtering quality) evaluations were recorded in the experiment. The experiment results show that if mean state updates per second is equal or greater than 20 in the task, SUT and SUPS can be decreased by adaptive-filtering policy without task performance affected obviously. However, if mean state updates per second is around 10 in the task, accompanied with SUT and SUPS decreased by adaptive-filtering policy, task performance also deteriorated badly