Machine learning-based selection of a representative video frame within a messaging application

A system comprising:

• at least one processor;
• and
• a memory storing instructions that, when executed by the at least one processor, configure the at least one processor to perform operations comprising:
• receiving a set of video frames corresponding to a video;
• determining a first subset of video frames by removing, from the set of video frames, those video frames which are outside of an image quality threshold;
• determining a second subset of video frames by removing, from the first subset of video frames, those video frames which are outside of an image stillness threshold;
• computing feature data for each video frame in the second subset of video frames;
• providing, for each video frame in the second subset of video frames, the feature data of the video frame as input to a machine learning model,
• wherein the machine learning model is configured to output a score for each video frame in the subset of video frames based on the feature data of the video frame, the machine learning model having been trained with a first set of images labeled based on image aesthetics, and further having been trained with second set of images labeled based on image quality;
• and
• selecting, from among the second subset of video frames, a video frame to represent the set of video frames based on the scores output by the machine learning model.

A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform operations comprising:

• receiving a set of video frames corresponding to a video;
• determining a first subset of video frames by removing, from the set of video frames, those video frames which are outside of an image quality threshold;
• determining a second subset of video frames by removing, from the first subset of video frames, those video frames which are or outside of an image stillness threshold;
• computing feature data for each video frame in the second subset of video frames;
• providing, for each video frame in the second subset of video frames, the feature data of the video frame as input to a machine learning model,
• wherein the machine learning model is configured to output a score for each video frame in the subset of video frames based on the feature data of the video frame, the machine learning model having been trained with a first set of images labeled based on image aesthetics, and further having been trained with second set of images labeled based on image quality;
• and
• selecting, from among the second subset of video frames, a video frame to represent the set of video frames based on the scores output by the machine learning model.