Machine learning networks for hybrid video compression and corresponding decompression

Abstract

Innovations in machine learning ('ML') networks used in video processing scenarios are described. For example, an ML refinement network can be used to refine video after a video decoder has reconstructed the video. Using the ML refinement network for post-processing can mitigate compression artifacts introduced during encoding and otherwise improve the quality of the reconstructed video. Or, as another example, an ML encoder network and ML decoder network can be used, in combination with a core video encoder and core video decoder, for hybrid compression and corresponding decompression. In the hybrid compression, the ML encoder network can transform video before encoding in order to boost rate-distortion performance of the core video encoder. In corresponding decompression, the ML decoder network can enhance reconstructed video after decoding, thereby compensating for transformations applied by the ML encoder network, mitigating compression artifacts, and otherwise improving the quality of the reconstructed video.