|
Machine learning algorithms for video processing typically work on frames (images) rather than video. In a typical use-case, FFmpeg can be used to extract images from video – in this example, a 50-frame sequence starting at 1:47: >ffmpeg -i input.vid -vf “select=’gte(t,107)*lt(selected_n,50)'” -vsync passthrough ‘107+%06d.png’ Omit the -vf option if extracting the entire video. . . . → Read More: Using FFmpeg to replace video frames Increase video resolution with an opensource machine learning algorithm for upscaling and interpolating video image frames using an automated command line script. Bringing machine learning algorithms a step closer to usability. Given a low-resolution video file, this script uses a machine-learning algorithm to increase (upscale) each frame’s resolution and optionally add (interpolate) an additional . . . → Read More: Upscale and interpolate video super-resolution using STARnet Increase video resolution with an opensource machine learning algorithm for upscaling video image frames using an automated command line script. Bringing machine learning algorithms a step closer to usability. Given a low-resolution video file, this script uses a machine-learning algorithm to increase (upscale) each frame’s resolution using information from neighbouring frames. The workhorse is . . . → Read More: Upscale video super-resolution using RBPN |
