Our enhanced video compression framework called EVC-CR which combine a conventional codec and a content-fitted restoration neural network (CRNN). In an encoder, CRNN is trained online with the reconstructed frames from a conventional encoder as the input and the raw frames as the ground truth. The content-fitting is realized by training it only with a group of consecutive frames. The content-fitted parameters of CRNN are transmitted as a part of an encoded bitstream. In a decoder, with the transmitted parameters, an inference operation of CRNN is performed on the decoded frames to generate the final result.

The following figure shows the performance gain of combining the proposed EVC-CR with the existing codecs. Experimental results show that the compression performance can be greatly improved in terms of 5.891% - 24.413% BD-BR reduction, by integrating CRNN with VTM, HM, X265, X264, and the end-to-end DNN codec DVC.

• pytorch >= 1.0

The experiment is divided into two processes, conventional encode and content-fitted restoration.

We tried five conventional codecs and an end-to-end deep video compression framework, including HM, x264, x265, NVENC, VTM, and DVC.

We provide a sequence BQTerrace including the original version and compressed version by HM to verify the effectiveness of our method. The data is available at Google Drive. You also can use your own data to verify it.

Testing the content-fitted sequence by the saved checkpoint:

python run.py -g 1 evaluate  -m CRNN -b 2 -c 16 -v BQTerrace_1920x1080_60 -q 37 --height 1080 --width 1920 --frame_num 500 --start_frame 0 --codec_mode RA --resume ./checkpoints/BQTerrace_1920x1080_60_QP37.pth

You also can encode and train the compressed video with the following command:

python run.py -g 1 train  -m CRNN -b 2 -c 16 -v BQTerrace_1920x1080_60 -q 37 --height 1080 --width 1920 --frame_num 500 --start_frame 0 --neighbor_frames 2 --max_epoch 200 --lr 0.001 --codec_mode RA

You can contact Lei Li by sending mail to [email protected]