This repository releases the test code for our paper
Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC (TMM 2020)
Weiyao Lin, Xiaoyi He, Xintong Han, Dong Liu, John See, Junni Zou, Hongkai Xiong, Feng Wu
git clone https://github.com/hexiaoyi95/Partition-aware
cd Partition-aware
pip install -r requirements.txt
2. Prepare test sequences. We provide an example on [One Drive](https://1drv.ms/u/s!AhjTb_4JKsIDiS5mwv5HTU36-k-F?e=x6OeAd). The original yuv sequences and compressed sequences are put into two different directories. If the original yuv sequence is named as *seq.yuv*, please name the compressed sequence at QP=37 as *seq_QP37.yuv*.
## Deploy a pre-trained model
- for yuv input post-processing:
```Shell
usage: inference.py [-h] [--QP QP] [--checkpoint CHECKPOINT]
[--test_num TEST_NUM] [--info INFO]
[--recYuv_path RECYUV_PATH] [--origYuv_path ORIGYUV_PATH]
[--patch_size PATCH_SIZE] [--Yonly]
optional arguments:
-h, --help show this help message and exit
--QP QP, -q QP test QP value
--checkpoint CHECKPOINT, -c CHECKPOINT
checkpoint to be evaluted
--test_num TEST_NUM, -n TEST_NUM
test frames number, default is 32
--info INFO output json filename
--recYuv_path RECYUV_PATH
reconstructed yuv dir
--origYuv_path ORIGYUV_PATH
original yuv dir
--patch_size PATCH_SIZE
patch_size, default is 64
--Yonly only test Y channel if specified
We released models for our partition-aware network and VRCNN+partition(i.e., 2-in+MM+AF and VRCNN+MM+AF in our paper) trained at QP=37 on One Drive
If you think this work is helpful for your own research, please consider add following bibtex config in your latex file
@article{lin2020partition,
title={Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC},
author={Lin, Weiyao and He, Xiaoyi and Han, Xintong and Liu, Dong and John, See and Zou, Junni and Xiong, Hongkai and Wu, Feng},
journal={IEEE Transaction on Multimedia},
doi={10.1109/TMM.2019.2962310},
year={2020},
organization={IEEE}
}