Systems and methods for hybrid machine learning and dct-based video compression

Abstract

Systems and methods for hybrid video compression. A method includes receiving an encoded video including at least two compressed frames corresponding to at least two anchor frames and a compressed subset of at least one intermediate frame between the at least two anchor frames, generating, by inputting the at least two anchor frames into a deep machine learning model, a synthesized image frame corresponding to the at least one intermediate frame between the at least two anchor frames, reconstructing at least one hybrid image frame by combining the compressed subset of the at least one intermediate frame with the synthesized image frame, and outputting a video including the at least two anchor frames and the at least one hybrid image frame. Thus, DCT-based video compression techniques can leverage machine learning-based video interpolation techniques to provide encoded video streams with a reduced bitrate and thus reduced bandwidth while maintaining image quality.

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