Time average is one effect which has many uses besides creating trail patterns of moving objects (figure~\ref{fig:timeaverage}).
The main use is reducing noise in still images (or in the motionless parts of a video). Merely point a video camera at a stationary subject for $30$ frames, capture the frames, and average them using time average and you will have a high quality print. In floating point color models, time average can increase the dynamic range of low quality cameras.
Inside the time average effect is an accumulation buffer and a divisor. A number of frames are accumulated in the \textit{accumulation} buffer and divided by the divisor to get the average (for $10$ accumulated frames the divisor is $10$). Because the time average can consume large amounts of memory, it is best applied by first disabling playback for the track, dropping the time average in it, configuring time average for the desired number of frames, and re-enabling playback for the track.
Time average is one effect which has many uses besides creating trail patterns of moving objects (figure~\ref{fig:timeaverage}).
The main use is reducing noise in still images (or in the motionless parts of a video). Merely point a video camera at a stationary subject for $30$ frames, capture the frames, and average them using time average and you will have a high quality print. In floating point color models, time average can increase the dynamic range of low quality cameras.
Inside the time average effect is an accumulation buffer and a divisor. A number of frames are accumulated in the \textit{accumulation} buffer and divided by the divisor to get the average (for $10$ accumulated frames the divisor is $10$). Because the time average can consume large amounts of memory, it is best applied by first disabling playback for the track, dropping the time average in it, configuring time average for the desired number of frames, and re-enabling playback for the track.