Microdosing for low bitrate video compression
A system includes a machine learning (ML) model-based video encoder configured to receive an uncompressed video sequence including multiple video frames, determine, from among the multiple video frames, a first video frame subset and a second video frame subset, encode the first video frame subset to produce a first compressed video frame subset, and identify a first decompression data for the first compressed video frame subset. The ML model-based video encoder is further configured to encode the second video frame subset to produce a second compressed video frame subset, and identify a second decompression data for the second compressed video frame subset. The first decompression data is specific to decoding the first compressed video frame subset but not the second compressed video frame subset, and the second decompression data is specific to decoding the second compressed video frame subset but not the first compressed video frame subset.
1. A system comprising:
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 ML model-based video encoder configured to:
receive an uncompressed video sequence including a plurality of video frames;
determine, from among the plurality of video frames, a first video frame subset and a second video frame subset;
encode the first video frame subset to produce the first compressed video frame subset;
identify the first decompression data for the first compressed video frame subset;
encode the second video frame subset to produce the second compressed video frame subset; and
identify the second decompression data for the second compressed video frame subset.
2. The system of claim 1 , wherein identifying the first decompression data comprises overfitting the first decompression data during the encoding of the first video frame subset, and wherein identifying the second decompression data comprises overfitting the second decompression data during the encoding of the second video frame subset.
3. The system of claim 1 , wherein:
the ML model-based video encoder is further configured to:
transmit, to the ML model-based video decoder, the first compressed video frame subset, the second compressed video frame subset, the first decompression data, and the second decompression data;
the ML model-based video decoder is configured to:
receive the first compressed video frame subset, the second compressed video frame subset, the first second decompression data, and the second decompression data;
decode the first compressed video frame subset using the first decompression data; and
decode the second compressed video frame subset using the second decompression data.
4. The system of claim 3 , wherein the first decompression data is received only once for decoding of the first compressed video frame subset, and wherein the second decompression data is received only once for decoding of the second compressed video frame subset.
5. The system of claim 1 , wherein the first decompression data is specific to decoding the first compressed video frame subset but not the second compressed video frame subset, and the second decompression data is specific to decoding the second compressed video frame subset but not the first compressed video frame subset.
6. The system of claim 1 , wherein the first decompression data and the second decompression data contain only weights of the MicroRN.
7. The system of claim 1 , wherein the ML model-based video encoder comprises a High-Fidelity Compression (HiFiC) encoder, and wherein the ML model-based video decoder includes at least ten times fewer parameters than a HiFiC decoder not using the first decompression data and the second decompression data.
8. The system of claim 1 , wherein the ML model-based video encoder comprises a HiFiC encoder, and wherein the ML model-based video decoder is fifty percent faster than a HiFiC decoder not using the first decompression data and the second decompression data.
9. 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.
10. The method of claim 9 , wherein identifying the first decompression data comprises overfitting the first decompression data during the encoding of the first video frame subset, and wherein identifying the second decompression data comprises overfitting the second decompression data during the encoding of the second video frame subset.
11. The method of claim 9 , further comprising:
transmitting, by the ML model-based video encoder, the first compressed video frame subset, second compressed video frame subset, the first decompression data, and second decompression data to an ML model-based video decoder;
receiving, by the ML model-based video decoder, the first compressed video frame subset, second compressed video frame subset, the first decompression data, and second decompression data;
decoding, by the ML model-based video decoder, the first compressed video frame subset using the first decompression data; and
decoding, by the ML model-based video decoder, the second compressed video frame subset using the second decompression data.
12. The method of claim 11 , wherein the first decompression data is received only once for decoding of the first compressed video frame subset, and wherein the second decompression data is received only once for decoding of the second compressed video frame subset.
13. The method of claim 11 , wherein the first decompression data is specific to decoding the first compressed video frame subset but not the second compressed video frame subset, and the second decompression data is specific to decoding the second compressed video frame subset but not the first compressed video frame subset.
14. The method of claim 9 , wherein the first decompression data and the second decompression data contain only weights of the MicroRN.
15. The method of claim 9 , wherein the ML model-based video encoder comprises a High-Fidelity Compression (HiFiC) encoder, and wherein the ML model-based video decoder includes at least ten times fewer parameters than a HiFiC decoder not using the first decompression data and the second decompression data.
16. The method of claim 9 , wherein the ML model-based video encoder comprises a HiFiC encoder, and wherein the ML model-based video decoder is fifty percent faster than a HiFiC decoder not using the first decompression data and the second decompression data.