1. A method comprising: identifying, via a computing device, a video, said video comprising a plurality of video frames each displaying an image; in response to identifying said video, automatically, via the computing device, performing frame filtering on the video by parsing, via the computing device, each of said video frames and identifying, based on said parsing, content of each image of each frame, said frame filtering further comprising determining, based on the content of each frame, a type of each frame, said frame filtering comprising identifying a first set of frames from said plurality of video frames based on said type of each frame; analyzing, via the computing device, said first set of frames by applying de-duplication software, said application comprising the computing device executing said de-duplication software, and based on said execution of the de-duplication software, determining duplicate frames within said first set of frames, said analysis further comprising, based on said duplicate frame determination, discarding said duplicate frames from said first set; computing, via the computing device, a stillness value for the remaining frames in the first set, said stillness value comprising an indication of motion energy present within content of the frames remaining in the first set; identifying, via the computing device, a second set of frames by extracting, via the computing device, keyframes from the remaining frames in said first set based on said stillness value, said extracted keyframes having a stillness value satisfying a threshold for motion energy, said extracted keyframes being a non-redundant subset of the video frames; determining, via the computing device, a relevance value for each frame in the second set, said relevance value determination comprising analyzing, via the computing device, aesthetic features of each frame in the second set and determining, based on said analysis, an aesthetic value for each frame, said determination further comprising clustering the frames in the second set based on a statistical gap between aesthetic values of the frames, and identifying a third frame set by selecting a frame from each cluster that has a highest aesthetic score; determining, via the computing device, a quality value of the frames in the third set based on a ranking of the aesthetic scores in the third set; and generating, via the computing device, a thumbnail image by selecting a frame in the third set having the highest aesthetic score, said thumbnail image comprising content of said selected frame.