5.Automosh modes
Lets understand automosh modes.
This slider show how much frames to delete from the video based on the max frame size. If your video has less or weird vector motion then you may not get any results. The solution in that case is to change the kill frame size to a lower value like 0.4 or 0.3. But lower than <0.2 is unstable (sometimes all the frames will get deleted).
This slider refers an n frame from the video and use it as reference based on the mosh type.
Formerly known as glitch size, this entry refers how long/often to glitch.
This option just ignores the first frame(s) in the glitch while corrupting.
This keeps the original audio if selected.
This is the classic convertor that is now removed from the python version. It can be used to get high quality exports but takes longer to render. It basically converts the video through a image sequence stream.
This is one time duplication effect, also moshes the video normaly: It duplicates c(count frame) times the p-frame number n (position frame)
Example: Bloom Example
Here I used Position Frame=2 and count frame(glitch size)=100 which means it duplicates 2nd frame 100 times
If you just want the normal datamosh like the moshup app does, then use this mode. It just removes the frame based on the kill frame size.
Paid version also includes a feature to view the iframe graph that will be deleted based on kill-frame size
This effect switches each consecutive frame witch each other.
It takes frame from around current position. position frame is spread size/amount.
It copy/deletes groups of count frames taken from every nth position.
It copies c frames starting from every n frame.
Example: if count frame=4 and position frame=2 then the sequence will be [1 2 3 4 3 4 5 6 5 6 7 8 7 8...]
This mode is modified version of bloom that continuously does the duplication effect for each frame n times. (Warning: Do not to put large values in the options). It creates a very long and slow video.