Benchmarking scikit-video
Fork me on GitHub

Benchmarking scikit-video

To be useful in any project, we would like to approach real-time performance. Some algorithms implemented may not be able to achieve this (exhaustive search algorithms, for example), but we still set our goals high!

All tests computed for scikit-video v1.1.1

Reading speed

Minimum time from 10 trials, loading the 3 test videos in skvideo.datasets:

Method Time (FFmpeg) 0.718217 seconds (LibAV) 0.815005 seconds (FFmpeg) 0.671952 seconds (LibAV) 0.774765 seconds

The fastest backend appears to be FFmpeg for both and Naturally, since uses a yield-based generator to supply frames, it is faster to use than which allocates space then copies data frame-by-frame.

Block Motion estimation

Using the default settings on the carphone_pristine.mp4 sequence, shape (120, 144, 176, 3)

Performance on block motion algorithms using skvideo.motion.blockMotion:

Method Time
exhaustive 43.946715 seconds
3-step search 17.558664 seconds
“new” 3-step search 32.340459 seconds
simple and efficient 3-step search 13.861776 seconds
4-step search 36.315580 seconds
adaptive rood pattern search 21.413136 seconds
diamond search 25.679036 seconds

We can see that 3-step search is currently the fastest algorithm at 17.5 seconds. That comes out to 0.146 seconds per frame, for 144x176 sized frames. Clearly this is not realtime, which makes sense given that the core computations are in non-optimized python.