Microdosing for low bitrate video compression

A method for use by a system including a machine learning (ML) model-based video encoder and an ML model-based video decoder comprising a degradation-aware block based Micro-Residual-Network (MicroRN) defined by a number of hidden channels and a number of degradation-aware blocks of the MicroRN, the MicroRN configured to decode a first compressed video frame subset using a first decompression data, and decode a second compressed video frame subset using a second decompression data, without utilizing a residual network of a generative adversarial network (GAN) trained decoder, the method comprising:

• receiving, by the ML model-based video encoder, an uncompressed video sequence including a plurality of video frames;
• determining, by the ML model-based video encoder from among the plurality of video frames, a first video frame subset and a second video frame subset;
• encoding, by the ML model-based video encoder, the first video frame subset to produce the first compressed video frame subset;
• identifying, by the ML model-based video encoder, the first decompression data for the first compressed video frame subset;
• encoding, by the ML model-based video encoder, the second video frame subset to produce the second compressed video frame subset;
• and
• identifying, by the ML model-based video encoder, the second decompression data for the second compressed video frame subset.