This repo contains the testing code for the paper in the ICCV 2021. "COMISR: Compression-Informed Video Super-Resolution"

Disclaimer: This is not an official Google product.

Install dependencies:

pip3 install -r requirements.txt

The vid4 testing data can be downloaded from: gs://gresearch/comisr/data/ gcloud sdk

The folder path should be similar to:
.../testdata/lr_crf25/calendar
.../testdata/lr_crf25/city
.../testdata/lr_crf25/foliage
.../testdata/lr_crf25/walk

.../testdata/hr/calendar
.../testdata/hr/city
.../testdata/hr/foliage
.../testdata/hr/walk

We use ffmpeg to compress video frames. Below is one sample CLI usage.

Suppose you have a sequence of frames in im%2d.png format, e.g. calendar from vid4.

ffmpeg -framerate 10 -i im%2d.png -c:v libx264 -crf 0 lossless.mp4 \
&& ffmpeg -i lossless.mp4 -vcodec libx264 -crf 25 crf25.mp4 \
&& ffmpeg -ss 00:00:00 -t 00:00:10 -i crf25.mp4 -r 10 crf25_%2d.png

The pre-trained model can be downloaded from: gs://gresearch/comisr/model/

python inference_and_eval.py \
--checkpoint_path=/tmp/model.ckpt \
--input_lr_dir=/tmp/lr_4x_crf25 \
--targets=/tmp/hr \
--output_dir=/tmp/output_dir

If you find this code is useful for your publication, please cite the original paper:

@inproceedings{yli_comisr_iccv2021,
  title = {COMISR: Compression-Informed Video Super-Resolution},
  author = {Yinxiao Li and Pengchong Jin and Feng Yang and Ce Liu and Ming-Hsuan Yang and Peyman Milanfar},
  booktitle = {ICCV},
  year = {2021}
}